learning multiple tasks with kernel methods we study the problem of learning many related tasks simultaneously using kernel methods and regularization the standard single task kernel methods such as support vector machines and regularization networks are extended to the case of multi task learning our analysis shows that the problem of estimating many task functions with regularization can be cast as a single task learning problem if a family of multi task kernel functions we define is used these kernels model relations among the tasks and are derived from a novel form of regularizers specific kernels that can be used for multi task learning are provided and experimentally tested on two real data sets in agreement with past empirical work on multi task learning the experiments show that learning multiple related tasks simultaneously using the proposed approach can significantly outperform standard single task learning particularly when there are many related tasks but few data per task

guided image completion by confidence propagation this paper presents a new guided image completion method which fills any missing values by considering information from a guidance image we develop a confidence propagation scheme that allows the filling process to be carried out simultaneously without the need of deciding the filling order we conduct experiments in several applications where the problem can be formulated into a guided image completion problem such as interactive segmentation and colorization the experimental results show that our method provides good results and succeeds in situations where conventional methods fail in addition as all building blocks are parallel processes our method is much suitable for hardware acceleration highlightswe proposed an image completion method based on a new formulation filling process was carried out simultaneously without deciding the filling order good results were obtained in a variety of applications

hierarchical multi label classification using fully associative ensemble learning developing a local hierarchical ensemble framework for hierarchical multi label classification hmc in which all the structural relationships in the class hierarchy are used to obtain global prediction introducing empirical loss minimization into hmc so that the learned model can capture the most useful information from historical data proposing sparse kernel and binary constraint hmc models traditional flat classification methods e g binary or multi class classification neglect the structural information between different classes in contrast hierarchical multi label classification hmc considers the structural information embedded in the class hierarchy and uses it to improve classification performance in this paper we propose a local hierarchical ensemble framework for hmc fully associative ensemble learning fael we model the relationship between each class nodes global prediction and the local predictions of all the class nodes as a multi variable regression problem with frobenius norm or l1 norm regularization it can be extended using the kernel trick which explores the complex correlation between global and local prediction in addition we introduce a binary constraint model to restrict the optimal weight matrix learning the proposed models have been applied to image annotation and gene function prediction datasets with tree structured class hierarchy and large scale visual recognition dataset with direct acyclic graph dag structured class hierarchy the experimental results indicate that our models achieve better performance when compared with other baseline methods

predictive updating methods with application to bayesian classification we propose algorithms based on random draws from predictive distributions of unknown quantities missing values for instance this procedure can either be iterative which is a special variation of the gibbs sampler or be sequential which is a variation of sequential imputation in the latter case one can update the posterior distribution with new observations easily the methods proposed have intuitive statistical implications and can be generalized to accommodate other bayesian like procedures we display some applications of the method in connection with the bayesian bootstrap classification hierarchical models and selection of variables in particular as an application of the method we present a unified treatment of switching regression models driven by a general binary process and we develop a bayesian testing procedure some simulations and a real example are used to illustrate the methods proposed

simultaneous structure and texture image inpainting an algorithm for the simultaneous filling in of texture and structure in regions of missing image information is presented in this paper the basic idea is to first decompose the image into the sum of two functions with different basic characteristics and then reconstruct each one of these functions separately with structure and texture filling in algorithms the first function used in the decomposition is of bounded variation representing the underlying image structure while the second function captures the texture and possible noise the region of missing information in the bounded variation image is reconstructed using image inpainting algorithms while the same region in the texture image is filled in with texture synthesis techniques the original image is then reconstructed adding back these two sub images the novel contribution of this paper is then in the combination of these three previously developed components image decomposition with inpainting and texture synthesis which permits the simultaneous use of filling in algorithms that are suited for different image characteristics examples on real images show the advantages of this proposed approach

robust estimation of mean squared error of small area estimators abstract a well known model due to fay and herriot for estimating small area domain means i is considered given i s it is assumed that the survey estimators y i are independent with means i and known variances di i 1 t further the i s are assumed to be independent with means x i and unknown variance a where x i is a vector of benchmark variables related to i and is a vector of regression parameters an empirical best linear unbiased prediction eblup estimator or an empirical linear bayes estimator t i y of i is obtained it is shown that an estimator of mean squared error mse of t t y derived by prasad and rao under normality of i and y i given i is robust with respect to nonnormality of the i s specifically it is shown that the prasad rao estimator of mse is correct to terms of order o t 1 for large t assuming only certain moment conditions on the i s and normally distributed survey errors results of a simulation study on the accur

multiview hessian regularization for image annotation the rapid development of computer hardware and internet technology makes large scale data dependent models computationally tractable and opens a bright avenue for annotating images through innovative machine learning algorithms semisupervised learning ssl therefore received intensive attention in recent years and was successfully deployed in image annotation one representative work in ssl is laplacian regularization lr which smoothes the conditional distribution for classification along the manifold encoded in the graph laplacian however it is observed that lr biases the classification function toward a constant function that possibly results in poor generalization in addition lr is developed to handle uniformly distributed data or single view data although instances or objects such as images and videos are usually represented by multiview features such as color shape and texture in this paper we present multiview hessian regularization mhr to address the above two problems in lr based image annotation in particular mhr optimally combines multiple hr each of which is obtained from a particular view of instances and steers the classification function that varies linearly along the data manifold we apply mhr to kernel least squares and support vector machines as two examples for image annotation extensive experiments on the pascal voc 07 dataset validate the effectiveness of mhr by comparing it with baseline algorithms including lr and hr

nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising part ii adaptive algorithms we combine the main ideas introduced in part i with adaptive techniques to arrive at a powerful algorithm that estimates missing data in nonstationary signals the proposed approach operates automatically based on a chosen linear transform that is expected to provide sparse decompositions over missing regions such that a portion of the transform coefficients over missing regions are zero or close to zero unlike prevalent algorithms our method does not necessitate any complex preconditioning segmentation or edge detection steps and it can be written as a progression of denoising operations we show that constructing estimates based on nonlinear approximants is fundamentally a nonconvex problem and we propose a progressive algorithm that is designed to deal with this issue directly the algorithm is applied to images through an extensive set of simulation examples primarily on missing regions containing textures edges and other image features that are not readily handled by established estimation and recovery methods we discuss the properties required of good transforms and in conjunction show the types of regions over which well known transforms provide good predictors we further discuss extensions of the algorithm where the utilized transforms are also chosen adaptively where unpredictable signal components in the progressions are identified and not predicted and where the prediction scenario is more general

on parametric bootstrap methods for small area prediction summary the particularly wide range of applications of small area prediction e g in policy making decisions has meant that this topic has received substantial attention in recent years the problems of estimating mean squared predictive error of correcting that estimator for bias and of constructing prediction intervals have been addressed by various workers although existing methodology is still restricted to a narrow range of models to overcome this difficulty we develop new bootstrap based methods which are applicable in very general settings for constructing bias corrected estimators of mean squared error and for computing prediction regions unlike existing techniques which are based largely on taylor expansions our bias corrected mean squared error estimators do not require analytical calculation they also have the property that they are non negative our prediction intervals have a high degree of coverage accuracy o n 3 where n is the number of areas if double bootstrap methods are employed the techniques do not depend on the form of the small area estimator and are applicable to general two level small area models where the variables at either level can be discrete or continuous and in particular can be non normal most importantly the new methods are simple and easy to apply

on measuring the variability of small area estimators under a basic area level model summary in this paper based on a basic area level model we obtain second order accurate approxi mations to the mean squared error of model based small area estimators using the fay herriot 1979 iterative method of estimating the model variance based on weighted residual sum of squares we also obtain mean squared error estimators unbiased to second order based on simulations we compare the finite sample performance of our mean squared error estimators with those based on method of moments maximum likelihood and residual maximum likelihood estimators of the model variance our results suggest that the fay herriot method performs better in terms of relative bias of mean squared error estimators than the other methods across different combinations of number of areas pattern of sampling variances and distribution of small area effects we also derive a noninformative prior on the model parameters for which the posterior variance of a small area mean is second order unbiased for the mean squared error the posterior variance based on such a prior possesses both bayesian and frequentist interpretations

missing texture reconstruction method based on error reduction algorithm using fourier transform magnitude estimation scheme a missing texture reconstruction method based on an error reduction er algorithm including a novel estimation scheme of fourier transform magnitudes is presented in this brief in our method fourier transform magnitude is estimated for a target patch including missing areas and the missing intensities are estimated by retrieving its phase based on the er algorithm specifically by monitoring errors converged in the er algorithm known patches whose fourier transform magnitudes are similar to that of the target patch are selected from the target image in the second approach the fourier transform magnitude of the target patch is estimated from those of the selected known patches and their corresponding errors consequently by using the er algorithm we can estimate both the fourier transform magnitudes and phases to reconstruct the missing areas

multiview vector valued manifold regularization for multilabel image classification in computer vision image datasets used for classification are naturally associated with multiple labels and comprised of multiple views because each image may contain several objects e g pedestrian bicycle and tree and is properly characterized by multiple visual features e g color texture and shape currently available tools ignore either the label relationship or the view complementarily motivated by the success of the vector valued function that constructs matrix valued kernels to explore the multilabel structure in the output space we introduce multiview vector valued manifold regularization rm mv 3 rm mr to integrate multiple features rm mv 3 rm mr exploits the complementary property of different features and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifold regularization we conduct extensive experiments on two challenging but popular datasets pascal voc 07 and mir flickr and validate the effectiveness of the proposed rm mv 3 rm mr for image classification

manifold regularization a geometric framework for learning from labeled and unlabeled examples we propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution we focus on a semi supervised framework that incorporates labeled and unlabeled data in a general purpose learner some transductive graph learning algorithms and standard methods including support vector machines and regularized least squares can be obtained as special cases we use properties of reproducing kernel hilbert spaces to prove new representer theorems that provide theoretical basis for the algorithms as a result in contrast to purely graph based approaches we obtain a natural out of sample extension to novel examples and so are able to handle both transductive and truly semi supervised settings we present experimental evidence suggesting that our semi supervised algorithms are able to use unlabeled data effectively finally we have a brief discussion of unsupervised and fully supervised learning within our general framework

the estimation of the mean squared error of small area estimators abstract small area estimation has received considerable attention in recent years because of a growing demand for reliable small area statistics the direct survey estimators based only on the data from a given small area or small domain are likely to yield unacceptably large standard errors because of small sample size in the domain therefore alternative estimators that borrow strength from other related small areas have been proposed in the literature to improve the efficiency these estimators use models either implicitly or explicitly that connect the small areas through supplementary e g census and administrative data for example simple synthetic estimators are based on implicit modeling in this article three small area models of battese harter and fuller 1988 dempster rubin and tsutakawa 1981 and fay and herriot 1979 are investigated these models are all special cases of a general mixed linear model involving fixed and random effects and a small area mean can be expr

clustering of multivariate binary data with dimension reduction via l1 regularized likelihood maximization clustering methods with dimension reduction have been receiving considerable wide interest in statistics lately and a lot of methods to simultaneously perform clustering and dimension reduction have been proposed this work presents a novel procedure for simultaneously determining the optimal cluster structure for multivariate binary data and the subspace to represent that cluster structure the method is based on a finite mixture model of multivariate bernoulli distributions and each component is assumed to have a low dimensional representation of the cluster structure this method can be considered as an extension of the traditional latent class analysis sparsity is introduced to the loading values which produces the low dimensional subspace for enhanced interpretability and more stable extraction of the subspace an em based algorithm is developed to efficiently solve the proposed optimization problem we demonstrate the effectiveness of the proposed method by applying it to a simulation study and real datasets highlightswe propose a novel clustering method of multivariate binary data the proposed method provides a low dimensional representation of clusters the proposed method overcomes the conventional tandem analysis the proposed method conducts feature selection for clustering

nonparametric estimation of mean squared prediction error in nested error regression models nested error regression models are widely used for analyzing clustered data for example they are often applied to two stage sample surveys and in biology and econometrics prediction is usually the main goal of such analyses and mean squared prediction error is the main way in which prediction performance is measured in this paper we suggest a new approach to estimating mean squared prediction error we introduce a matched moment double bootstrap algorithm enabling the notorious underestimation of the naive mean squared error estimator to be substantially reduced our approach does not require specific assumptions about the distributions of errors additionally it is simple and easy to apply this is achieved through using monte carlo simulation to implicitly develop formulae which in a more conventional approach would be derived laboriously by mathematical arguments

ensemble manifold regularization we propose an automatic approximation of the intrinsic manifold for general semi supervised learning ssl problems unfortunately it is not trivial to define an optimization function to obtain optimal hyperparameters usually cross validation is applied but it does not necessarily scale up other problems derive from the suboptimality incurred by discrete grid search and the overfitting therefore we develop an ensemble manifold regularization emr framework to approximate the intrinsic manifold by combining several initial guesses algorithmically we designed emr carefully so it 1 learns both the composite manifold and the semi supervised learner jointly 2 is fully automatic for learning the intrinsic manifold hyperparameters implicitly 3 is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption and 4 is scalable for a large number of candidate manifold hyperparameters from both time and space perspectives furthermore we prove the convergence property of emr to the deterministic matrix at rate root n extensive experiments over both synthetic and real data sets demonstrate the effectiveness of the proposed framework

estimation of finite population domain means a model assisted empirical best prediction approach in this article we introduce a general methodology for producing a model assisted empirical best predictor ebp of a finite population domain mean using data from a complex survey our method improves on the commonly used design consistent survey estimator by using a suitable mixed model such a model combines information from related sources such as census and administrative data unlike a purely model based ebp the proposed model assisted ebp converges in probability to the customary design consistent estimator as the domain and sample sizes increase the convergence in probability is shown to hold with respect to the sampling design irrespective of the assumed mixed model a property commonly known as design consistency this property ensures robustness of the proposed predictor against possible model failures in addition the convergence in probability is shown to be valid with respect to the assumed mixed model model consistency a new mean squared prediction error mspe estimator is proposed

principal components null space analysis for image and video classification we present a new classification algorithm principal component null space analysis pcnsa which is designed for classification problems like object recognition where different classes have unequal and nonwhite noise covariance matrices pcnsa first obtains a principal components subspace pca space for the entire data in this pca space it finds for each class i an m sub i dimensional subspace along which the class intraclass variance is the smallest we call this subspace an approximate null space ans since the lowest variance is usually much smaller than the highest a query is classified into class i if its distance from the class mean in the class ans is a minimum we derive upper bounds on classification error probability of pcnsa and use these expressions to compare classification performance of pcnsa with that of subspace linear discriminant analysis slda we propose a practical modification of pcnsa called progressive pcnsa that also detects new untrained classes finally we provide an experimental comparison of pcnsa and progressive pcnsa with slda and pca and also with other classification algorithms linear svms kernel pca kernel discriminant analysis and kernel slda for object recognition and face recognition under large pose expression variation we also show applications of pcnsa to two classification problems in video an action retrieval problem and abnormal activity detection

image denoising via sparse and redundant representations over learned dictionaries we address the image denoising problem where zero mean white and homogeneous gaussian additive noise is to be removed from a given image the approach taken is based on sparse and redundant representations over trained dictionaries using the k svd algorithm we obtain a dictionary that describes the image content effectively two training options are considered using the corrupted image itself or training on a corpus of high quality image database since the k svd is limited in handling small image patches we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image we show how such bayesian treatment leads to a simple and effective denoising algorithm this leads to a state of the art denoising performance equivalent and sometimes surpassing recently published leading alternative denoising methods

multiview matrix completion for multilabel image classification there is growing interest in multilabel image classification due to its critical role in web based image analytics based applications such as large scale image retrieval and browsing matrix completion mc has recently been introduced as a method for transductive semisupervised multilabel classification and has several distinct advantages including robustness to missing data and background noise in both feature and label space however it is limited by only considering data represented by a single view feature which cannot precisely characterize images containing several semantic concepts to utilize multiple features taken from different views we have to concatenate the different features as a long vector however this concatenation is prone to over fitting and often leads to very high time complexity in mc based image classification therefore we propose to weightedly combine the mc outputs of different views and present the multiview mc mvmc framework for transductive multilabel image classification to learn the view combination weights effectively we apply a cross validation strategy on the labeled set in particular mvmc splits the labeled set into two parts and predicts the labels of one part using the known labels of the other part the predicted labels are then used to learn the view combination coefficients in the learning process we adopt the average precision ap loss which is particular suitable for multilabel image classification since the ranking based criteria are critical for evaluating a multilabel classification system a least squares loss formulation is also presented for the sake of efficiency and the robustness of the algorithm based on the ap loss compared with the other losses is investigated experimental evaluation on two real world data sets pascal voc 07 and mir flickr demonstrate the effectiveness of mvmc for transductive semisupervised multilabel image classification and show that mvmc can exploit complementary properties of different features and output consistent labels for improved multilabel image classification

annihilating filter based low rank hankel matrix approach for image inpainting in this paper we propose a patch based image inpainting method using a low rank hankel structured matrix completion approach the proposed method exploits the annihilation property between a shift invariant filter and image data observed in many existing inpainting algorithms in particular by exploiting the commutative property of the convolution the annihilation property results in a low rank block hankel structure data matrix and the image inpainting problem becomes a low rank structured matrix completion problem the block hankel structured matrices are obtained patch by patch to adapt to the local changes in the image statistics to solve the structured low rank matrix completion problem we employ an alternating direction method of multipliers with factorization matrix initialization using the low rank matrix fitting algorithm as a side product of the matrix factorization locally adaptive dictionaries can be also easily constructed despite the simplicity of the algorithm the experimental results using irregularly subsampled images as well as various images with globally missing patterns showed that the proposed method outperforms existing state of the art image inpainting methods

image inpainting by patch propagation using patch sparsity this paper introduces a novel examplar based inpainting algorithm through investigating the sparsity of natural image patches two novel concepts of sparsity at the patch level are proposed for modeling the patch priority and patch representation which are two crucial steps for patch propagation in the examplar based inpainting approach first patch structure sparsity is designed to measure the confidence of a patch located at the image structure e g the edge or corner by the sparseness of its nonzero similarities to the neighboring patches the patch with larger structure sparsity will be assigned higher priority for further inpainting second it is assumed that the patch to be filled can be represented by the sparse linear combination of candidate patches under the local patch consistency constraint in a framework of sparse representation compared with the traditional examplar based inpainting approach structure sparsity enables better discrimination of structure and texture and the patch sparse representation forces the newly inpainted regions to be sharp and consistent with the surrounding textures experiments on synthetic and natural images show the advantages of the proposed approach

robust empirical bayes estimation of means from stratified samples abstract this article considers simultaneous estimation of means from several strata where each stratum contains a finite number of elements for example quite often estimates of annual incomes unemployment rates and so forth must be made simultaneously for many areas our results find application in small area estimation where very few samples are available from an individual area and an estimate of a certain area mean or simultaneous estimates of several area means can be improved by incorporating information from similar neighboring areas ghosh and meeden 1986 considered empirical bayes estimation of the finite population mean assuming a normal superpopulation model their estimators generalize automatically when one estimates a vector of stratum means this article relaxes the normality assumption and assumes instead that the posterior expectation of any stratum mean is a linear function of the sample observations this is the so called posterior linearity property as described in ericson 1

small area estimation with uncertain random effects random effects models play an important role in model based small area estimation random effects account for any lack of fit of a regression model for the population means of small areas on a set of explanatory variables in a recent article datta hall and mandal showed that if the random effects can be dispensed with via a suitable test then the model parameters and the small area means may be estimated with substantially higher accuracy the work of datta hall and mandal is most useful when the number of small areas m is moderately large for large m the null hypothesis of no random effects will likely be rejected rejection of the null hypothesis is usually caused by a few large residuals signifying a departure of the direct estimator from the synthetic regression estimator as a flexible alternative to the fay herriot random effects model and the approach in datta hall and mandal in this article we consider a mixture model for random effects it is reasonably expected that small areas wit

a decoupled method for image inpainting with patch based low rank regulariztion abstract in this paper we propose a decoupled variational method for image inpainting in both image domain and transform domain including wavelet domain and fourier domain the original image inpainting problem is decoupled as two minimization problems with different energy functionals one is image denoising with low rank regularization method i e the patch based weighted nuclear norm minimization pwnnm the other is linear combination in image domain or transform domain an iterative algorithm is then obtained by minimizing the two problems alternatingly in particular we derive the variational formulas for pwnnm and reformulate the denoising process into three steps image decomposition patch matrix denoising and image reconstruction the convergence of the numerical algorithm is proved under some assumptions the numerical experiments and comparisons on various images demonstrate the effectiveness of the proposed methods

natural exponential families with quadratic variance functions statistical theory the normal poisson gamma binomial negative binomial and nefghs distributions are the six univariate natural exponential families nef with quadratic variance functions qvf this sequel to morris 1982 treats certain statistical topics that can be handled within this unified nef qvf formulation including unbiased estimation bhattacharyya and cramer rao lower bounds conditional distributions and moments quadratic regression conjugate prior distributions moments of conjugate priors and posterior distributions empirical bayes and g 2 minimax marginal distributions and their moments parametric empirical bayes and characterizations

fusion of multichannel local and global structural cues for photo aesthetics evaluation photo aesthetic quality evaluation is a fundamental yet under addressed task in computer vision and image processing fields conventional approaches are frustrated by the following two drawbacks first both the local and global spatial arrangements of image regions play an important role in photo aesthetics however existing rules e g visual balance heuristically define which spatial distribution among the salient regions of a photo is aesthetically pleasing second it is difficult to adjust visual cues from multiple channels automatically in photo aesthetics assessment to solve these problems we propose a new photo aesthetics evaluation framework focusing on learning the image descriptors that characterize local and global structural aesthetics from multiple visual channels in particular to describe the spatial structure of the image local regions we construct graphlets small sized connected graphs by connecting spatially adjacent atomic regions since spatially adjacent graphlets distribute closely in their feature space we project them onto a manifold and subsequently propose an embedding algorithm the embedding algorithm encodes the photo global spatial layout into graphlets simultaneously the importance of graphlets from multiple visual channels are dynamically adjusted finally these post embedding graphlets are integrated for photo aesthetics evaluation using a probabilistic model experimental results show that 1 the visualized graphlets explicitly capture the aesthetically arranged atomic regions 2 the proposed approach generalizes and improves four prominent aesthetic rules and 3 our approach significantly outperforms state of the art algorithms in photo aesthetics prediction

exemplar based inpainting technical review and new heuristics for better geometric reconstructions this paper proposes a technical review of exemplar based inpainting approaches with a particular focus on greedy methods several comparative and illustrative experiments are provided to deeply explore and enlighten these methods and to have a better understanding on the state of the art improvements of these approaches from this analysis three improvements over criminisi et al algorithm are then presented and detailed 1 a tensor based data term for a better selection of pixel candidates to fill in 2 a fast patch lookup strategy to ensure a better global coherence of the reconstruction and 3 a novel fast anisotropic spatial blending algorithm that reduces typical block artifacts using tensor models relevant comparisons with the state of the art inpainting methods are provided that exhibit the effectiveness of our contributions

image inpainting using reproducing kernel hilbert space and heaviside functions image inpainting a technique of repairing damaged images is an important topic in image processing in this paper we solve the problem from an intensity function estimation perspective we assume the underlying image is defined on a continuous domain and belongs to a space spanned by a basis of a reproducing kernel hilbert space and some variations of the heaviside function the reproducing kernel hilbert space is used to model the smooth component of the image while heaviside function variations are used to model the edges the coefficients of the redundant basis are computed by the discrete intensity at undamaged domain we test the proposed model through various images numerical experiments show the effectiveness of the proposed method especially in recovering edges

a group based image inpainting using patch refinement in mrf framework this paper presents a markov random field mrf based image inpainting algorithm using patch selection from groups of similar patches and optimal patch assignment through joint patch refinement in patch selection a novel group formation strategy based on subspace clustering is introduced to search the candidate patches in relevant source region only this improves patch searching in terms of both quality and time we also propose an efficient patch refinement scheme using higher order singular value decomposition to capture underlying pattern among the candidate patches this eliminates random variation and unwanted artifacts as well finally a weight term is computed based on the refined patches and is incorporated in the objective function of the mrf model to improve the optimal patch assignment experimental results on a large number of natural images and comparison with well known existing methods demonstrate the efficacy and superiority of the proposed method

hierarchical super resolution based inpainting this paper introduces a novel framework for examplar based inpainting it consists in performing first the inpainting on a coarse version of the input image a hierarchical super resolution algorithm is then used to recover details on the missing areas the advantage of this approach is that it is easier to inpaint low resolution pictures than high resolution ones the gain is both in terms of computational complexity and visual quality however to be less sensitive to the parameter setting of the inpainting method the low resolution input picture is inpainted several times with different configurations results are efficiently combined with a loopy belief propagation and details are recovered by a single image super resolution algorithm experimental results in a context of image editing and texture synthesis demonstrate the effectiveness of the proposed method results are compared to five state of the art inpainting methods

bayesian versus frequentist measures of error in small area estimation summary this paper compares analytically and empirically the frequentist and bayesian measures of error in small area estimation the model postulated is the nested error regression model which allows for random small area effects to represent the joint effect of small area characteristics that are not accounted for by the fixed regressor variables when the variance components are known then under a uniform prior for the regression coefficients and normality of the error terms the frequentist and the bayesian approaches yield the same predictors and prediction mean squared errors mses defined accordingly when the variance components are unknown it is common practice to replace the unknown variances by sample estimates in the expressions for the optimal predictors so that the resulting empirical predictors remain the same under the two approaches the use of this paradigm requires however modifications to the expressions of the prediction mse to account for the extra variability induced by the need to estimate the variance components the main focus of this paper is to review and compare the modifications to prediction mses proposed in the literature under the two approaches with special emphasis on the orders of the bias of the resulting approximations to the true mses some new approximations based on monte carlo simulation are also proposed and compared with the existing methods the advantage of these approximations is their simplicity and generality finite sample frequentist properties of the various methods are explored by a simulation study the main conclusions of this study are that the use of second order bias corrections generally yields better results in terms of the bias of the mse approximations and the coverage properties of confidence intervals for the small area means the bayesian methods are found to have good frequentist properties but they can be inferior to the frequentist methods the second order approximations under both approaches have however larger variances than the corresponding first order approximations which in most cases result in higher mses of the mse approximations

image inpainting through metric labeling via guided patch mixing in this paper we present a novel formulation of exemplar based image inpainting as a metric labeling problem and solve it through the simulated annealing algorithm due to their greedy nature exemplar based methods sometimes produce inpainted images which are visually inconsistent these methods are highly dependent upon the initialization to solve these problems we generate five images with a different initialization a suitable mixture of these five images produces a good inpainted image the cost function of the proposed metric labeling problem consists of three components namely neighbor cost total variation cost and structure cost a linear combination among these components is used to maintain better visual consistency in the inpainted region having smooth transition from the bordering regions of the source image we use a quality measure to this end our experiments on a wide variety of images demonstrate that the proposed technique produces better inpainting images as compared with some other state of the art techniques

manifold regularized multitask learning for semi supervised multilabel image classification it is a significant challenge to classify images with multiple labels by using only a small number of labeled samples one option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution however such an approach does not perform well in practice when images from multiple concepts are represented by high dimensional visual features thus manifold regularization is insufficient to control the model complexity in this paper we propose a manifold regularized multitask learning mrmtl algorithm mrmtl learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks it effectively controls the model complexity because different tasks limit one another s search volume and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold we conduct extensive experiments on the pascal voc 07 dataset with 20 classes and the mir dataset with 38 classes by comparing mrmtl with popular image classification algorithms the results suggest that mrmtl is effective for image classification

confidence sets centered at james stein estimators a surprise concerning the unknown variance case abstract we compare the confidence set centered at a james stein point estimator to the usual f confidence set for the p regression parameters of a linear model previous studies usually focused on the known variance case and typically concluded that whatever holds in the known variance case also holds in the unknown variance case when the variance is replaced by its best linear estimator based on s 2 where s 2 is the sum of squarred residuals we are surprised that this is not entirely the scenario we observe here in fact in many situations involving unknown variance the range of the shrinkage factor a for the associated confidence set to have uniformly higher coverage probabilities than its f counterpart can be ten times bigger than 2 p 2 the expected upper bound in the known variance case this is true especially when the degrees of freedom are small our numerical studies also show that to gain substantial improvement one has to use a much larger a especially when the degrees of freedom of the residuals are small application to an economic data set is also included

a random effects regression model for meta analysis many meta analyses use a random effects model to account for heterogeneity among study results beyond the variation associated with fixed effects a random effects regression approach for the synthesis of 2 x 2 tables allows the inclusion of covariates that may explain heterogeneity a simulation study found that the random effects regression method performs well in the context of a meta analysis of the efficacy of a vaccine for the prevention of tuberculosis where certain factors are thought to modify vaccine efficacy a smoothed estimator of the within study variances produced less bias in the estimated regression coefficients the method provided very good power for detecting a non zero intercept term representing overall treatment efficacy but low power for detecting a weak covariate in a meta analysis of 10 studies we illustrate the model by exploring the relationship between vaccine efficacy and one factor thought to modify efficacy the model also applies to the meta analysis of continuous outcomes when covariates are present

model selection by testing for the presence of small area effects and application to area level data the models used in small area inference often involve unobservable random effects while this can significantly improve the adaptivity and flexibility of a model it also increases the variability of both point and interval estimators if we could test for the existence of the random effects and if the test were to show that they were unlikely to be present then we would arguably not need to incorporate them into the model and thus could significantly improve the precision of the methodology in this article we suggest an approach of this type we develop simple bootstrap methods for testing for the presence of random effects applicable well beyond the conventional context of the natural exponential family if the null hypothesis that the effects are not present is not rejected then our general methodology immediately gives us access to estimators of unknown model parameters and estimators of small area means such estimators can be substantially more effective for example because they enjoy much fa

low rank representation with adaptive distance penalty for semi supervised subspace classification abstract the graph based semi supervised subspace learning ssl methods treat both labeled and unlabeled data as nodes in a graph and then instantiate edges among these nodes by weighting the affinity between the corresponding pairs of samples constructing a good graph to discover the intrinsic structures of the data is critical for these ssl tasks such as subspace clustering and classification the low rank representation lrr is one of powerful subspace clustering methods based on which a weighted affinity graph can be constructed generally adjacent samples usually belong to a union of subspace and thereby nearby points in the graph should have large edge weights motivated by this in this paper we proposed a novel lrr with adaptive distance penalty lrradp to construct a good affinity graph the graph identified by the lrradp can not only capture the global subspace structure of the whole data but also effectively preserve the neighbor relationship among samples furthermore by projecting the data set into an appropriate subspace the lrradp can be further improved to construct a more discriminative affinity graph extensive experiments on different types of baseline datasets are carried out to demonstrate the effectiveness of the proposed methods the improved method named as lrradp2 shows impressive performance on real world handwritten and noisy data the matlab codes of the proposed methods will be available at http www yongxu org lunwen html

parametric empirical bayes inference theory and applications abstract this article reviews the state of multiparameter shrinkage estimators with emphasis on the empirical bayes viewpoint particularly in the case of parametric prior distributions some successful applications of major importance are considered recent results concerning estimates of error and confidence intervals are described and illustrated with data

a universal variational framework for sparsity based image inpainting in this paper we extend an existing universal variational framework for image inpainting with new numerical algorithms given certain regularization operator and denoting u the latent image the basic model is to minimize the l p p 0 1 norm of u preserving the pixel values outside the inpainting region utilizing the operator splitting technique the original problem can be approximated by a new problem with extra variable with the alternating minimization method the new problem can be decomposed as two subproblems with exact solutions there are many choices for in our approach such as gradient operator wavelet transform framelet transform or other tight frames moreover with slight modification we can decouple our framework into two relatively independent parts 1 denoising and 2 linear combination therefore we can take any denoising method including bm3d filter in the denoising step the numerical experiments on various image inpainting tasks such as scratch and text removal randomly missing pixel filling and block completion clearly demonstrate the super performance of the proposed methods furthermore the theoretical convergence of the proposed algorithms is proved

compressive projection principal component analysis principal component analysis pca is often central to dimensionality reduction and compression in many applications yet its data dependent nature as a transform computed via expensive eigendecomposition often hinders its use in severely resource constrained settings such as satellite borne sensors a process is presented that effectively shifts the computational burden of pca from the resource constrained encoder to a presumably more capable base station decoder the proposed approach compressive projection pca cppca is driven by projections at the sensor onto lower dimensional subspaces chosen at random while the cppca decoder given only these random projections recovers not only the coefficients associated with the pca transform but also an approximation to the pca transform basis itself an analysis is presented that extends existing rayleigh ritz theory to the special case of highly eccentric distributions this analysis in turn motivates a reconstruction process at the cppca decoder that consists of a novel eigenvector reconstruction based on a convex set optimization driven by ritz vectors within the projected subspaces as such cppca constitutes a fundamental departure from traditional pca in that it permits its excellent dimensionality reduction and compression performance to be realized in an light encoder heavy decoder system architecture in experimental results cppca outperforms a multiple vector variant of compressed sensing for the reconstruction of hyperspectral data

standard errors of prediction in generalized linear mixed models abstract the unconditional mean squared error of prediction umsep is widely used as a measure of prediction variance for inferences concerning linear combinations of fixed and random effects in the classical normal theory mixed model but the umsep is inappropriate for generalized linear mixed models where the conditional variance of the random effects depends on the data when the random effects describe variation between independent small domains and domain specific prediction is of interest we propose a conditional mean squared error of prediction cmsep as a general measure of prediction variance the cmsep is shown to be the sum of the conditional variance and a positive correction that accounts for the sampling variability of parameter estimates we derive a second order correct estimate of the cmsep that consists of three components a a plug in estimate of the conditional variance b a plug in estimate of a taylor series approximation to the correction term and c a bootstrap estimate of

mean squared error estimation in transformed fay herriot models summary the problem of accurately estimating the mean squared error of small area estimators within a fay herriot normal error model is studied theoretically in the common setting where the model is fitted to a logarithmically transformed response variable for bias corrected empirical best linear unbiased predictor small area point estimators mean squared error formulae and estimators are provided with biases of smaller order than the reciprocal of the number of small areas the performance of these mean squared error estimators is illustrated by a simulation study and a real data example relating to the county level estimation of child poverty rates in the us census bureau s on going small area income and poverty estimation project

a probabilistic associative model for segmenting weakly supervised images weakly supervised image segmentation is an important yet challenging task in image processing and pattern recognition fields it is defined as in the training stage semantic labels are only at the image level without regard to their specific object scene location within the image given a test image the goal is to predict the semantics of every pixel superpixel in this paper we propose a new weakly supervised image segmentation model focusing on learning the semantic associations between superpixel sets graphlets in this paper in particular we first extract graphlets from each image where a graphlet is a small sized graph measures the potential of multiple spatially neighboring superpixels i e the probability of these superpixels sharing a common semantic label such as the sky or the sea to compare different sized graphlets and to incorporate image level labels a manifold embedding algorithm is designed to transform all graphlets into equal length feature vectors finally we present a hierarchical bayesian network to capture the semantic associations between postembedding graphlets based on which the semantics of each superpixel is inferred accordingly experimental results demonstrate that 1 our approach performs competitively compared with the state of the art approaches on three public data sets and 2 considerable performance enhancement is achieved when using our approach on segmentation based photo cropping and image categorization

nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising part i theory we study the robust estimation of missing regions in images and video using adaptive sparse reconstructions our primary application is on missing regions of pixels containing textures edges and other image features that are not readily handled by prevalent estimation and recovery algorithms we assume that we are given a linear transform that is expected to provide sparse decompositions over missing regions such that a portion of the transform coefficients over missing regions are zero or close to zero we adaptively determine these small magnitude coefficients through thresholding establish sparsity constraints and estimate missing regions in images using information surrounding these regions unlike prevalent algorithms our approach does not necessitate any complex preconditioning segmentation or edge detection steps and it can be written as a sequence of denoising operations we show that the region types we can effectively estimate in a mean squared error sense are those for which the given transform provides a close approximation using sparse nonlinear approximants we show the nature of the constructed estimators and how these estimators relate to the utilized transform and its sparsity over regions of interest the developed estimation framework is general and can readily be applied to other nonstationary signals with a suitable choice of linear transforms part i discusses fundamental issues and part ii is devoted to adaptive algorithms with extensive simulation examples that demonstrate the power of the proposed techniques

bayesian estimation of a multilevel irt model using gibbs sampling in this article a two level regression model is imposed on the ability parameters in an item response theory irt model the advantage of using latent rather than observed scores as dependent variables of a multilevel model is that it offers the possibility of separating the influence of item difficulty and ability level and modeling response variation and measurement error another advantage is that contrary to observed scores latent scores are test independent which offers the possibility of using results from different tests in one analysis where the parameters of the irt model and the multilevel model can be concurrently estimated the two parameter normal ogive model is used for the irt measurement model it will be shown that the parameters of the two parameter normal ogive model and the multilevel model can be estimated in a bayesian framework using gibbs sampling examples using simulated and real data are given

regression analysis with linked data record linkage or exact matching can be used to join together two files that contain information on the same individuals but lack unique personal identification codes the possibility of errors in linkage causes problems for estimating the relationships between variables on the two files the effect is analogous to the impact of measurement error a model of a linear regression relationship between variables in linked files is proposed assuming the probabilities that pairs of records are links are known an unbiased estimator of the regression coefficients is derived methods for estimating the linkage probabilities by using mixture models are discussed a consistent estimator of the covariance matrix of the proposed estimator is proposed a bootstrap estimator is used to reflect the impact of the uncertainty in record linkage model parameters on the estimators of the regression parameters a simulation study compares the performance of the proposed estimator and alternatives

context aware patch based image inpainting using markov random field modeling in this paper we first introduce a general approach for context aware patch based image inpainting where textural descriptors are used to guide and accelerate the search for well matching candidate patches a novel top down splitting procedure divides the image into variable size blocks according to their context constraining thereby the search for candidate patches to nonlocal image regions with matching context this approach can be employed to improve the speed and performance of virtually any patch based inpainting method we apply this approach to the so called global image inpainting with the markov random field mrf prior where mrf encodes a priori knowledge about consistency of neighboring image patches we solve the resulting optimization problem with an efficient low complexity inference method experimental results demonstrate the potential of the proposed approach in inpainting applications like scratch text and object removal improvement and significant acceleration of a related global mrf based inpainting method is also evident

empirical likelihood for small area estimation abstract small areaestimation has become a topic of growingimportance inrecent years due to increasing demand for small area statistics fromboth the public and private sectors the current methodologies inthis area with possibly a handful of exceptions are all parametric be it bayesian or frequentist even the so called nonparamet ric procedures based on empirical best linear unbiased predictors eblup assume linearity of the estimators of small area means resampling methods like jacknife and bootstrap are used mostlyfor mean squared error estimation in this paper we propose analternative bayesian approach based on empirical likelihood whichdoes not require the assumption of either a parametric likelihoodor linearity of estimators the proposed method can also handleboth discrete and continuous data in a uni ed manner empiricallikelihood for both area and unit level models are introduced theimproved performance of our estimator is illustrated through twoexamples involving both discrete and continuous data key words and phrases area level exponentially tilted t prior unit level

multitask linear discriminant analysis for view invariant action recognition robust action recognition under viewpoint changes has received considerable attention recently to this end self similarity matrices ssms have been found to be effective view invariant action descriptors to enhance the performance of ssm based methods we propose multitask linear discriminant analysis lda a novel multitask learning framework for multiview action recognition that allows for the sharing of discriminative ssm features among different views i e tasks inspired by the mathematical connection between multivariate linear regression and lda we model multitask multiclass lda as a single optimization problem by choosing an appropriate class indicator matrix in particular we propose two variants of graph guided multitask lda 1 where the graph weights specifying view dependencies are fixed a priori and 2 where graph weights are flexibly learnt from the training data we evaluate the proposed methods extensively on multiview rgb and rgbd video data sets and experimental results confirm that the proposed approaches compare favorably with the state of the art

sparse representation for color image restoration sparse representations of signals have drawn considerable interest in recent years the assumption that natural signals such as images admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data in particular the design of well adapted dictionaries for images has been a major challenge the k svd has been recently proposed for this task and shown to perform very well for various grayscale image processing tasks in this paper we address the problem of learning dictionaries for color images and extend the k svd based grayscale image denoising algorithm that appears in this work puts forward ways for handling nonhomogeneous noise and missing information paving the way to state of the art results in applications such as color image denoising demosaicing and inpainting as demonstrated in this paper

filling in by joint interpolation of vector fields and gray levels a variational approach for filling in regions of missing data in digital images is introduced the approach is based on joint interpolation of the image gray levels and gradient isophotes directions smoothly extending in an automatic fashion the isophote lines into the holes of missing data this interpolation is computed by solving the variational problem via its gradient descent flow which leads to a set of coupled second order partial differential equations one for the gray levels and one for the gradient orientations the process underlying this approach can be considered as an interpretation of the gestaltist s principle of good continuation no limitations are imposed on the topology of the holes and all regions of missing data can be simultaneously processed even if they are surrounded by completely different structures applications of this technique include the restoration of old photographs and removal of superimposed text like dates subtitles or publicity examples of these applications are given we conclude the paper with a number of theoretical results on the proposed variational approach and its corresponding gradient descent flow

color direction patch sparsity based image inpainting using multidirection features this paper proposes a color direction patch sparsity based image inpainting method to better maintain structure coherence texture clarity and neighborhood consistence of the inpainted region of an image the method uses super wavelet transform to estimate the multi direction features of a degraded image and combines with color information to construct the weighted color direction distance wcdd to measure the difference between two patches based on the wcdd the color direction structure sparsity is defined to obtain a more robust filling order and more suitable multiple candidate patches are searched then the target patches are sparsely represented by the multiple candidate patches under neighborhood consistency constraints in both the color and the multi direction spaces experimental results are presented to demonstrate the effectiveness of the proposed approach on tasks such as scratch removal text removal block removal and object removal the effects of super wavelet transforms and direction features are also investigated

a sample reuse method for accurate parametric empirical bayes confidence intervals abstract parametric empirical bayes methods of point estimation for a vector of unknown parameters date to the landmark paper of james and stein 1961 the usual approach is to use the mean of the estimated posterior distribution of each parameter where the estimation of the prior parameters hyperparameters is accomplished through the marginal distribution of the data while point estimates computed this way usually perform well interval estimates based on the estimated posterior called naive eb intervals are not they fail to account for the variability in the estimation of the hyperparameters generally resulting in sub nominal coverage probability in the eb sense defined in morris 1983a in this paper we extend the work of carlin and gelfand 1989 who proposed a conditional bias correction method for developing eb intervals which corrects the deficiencies in the naive intervals we show how bias correction can be implemented in general via a type iii parametric bootstrap procedure a sample reuse method first employed by laird and louis 1987

an approach for multi label classification by directed acyclic graph with label correlation maximization traditional supervised learning approaches primarily work in the single label environment however in many real world problems data instances are usually associated with multiple labels simultaneously and multi label learning is increasingly required in many modern applications in multi label learning the key to successful classification is effectively exploiting the complex correlations among the output labels this paper proposes a novel multi label learning method inspired by the classifier chain approach the main contribution of this work is to model the correlations of the labels using a directed acyclic graph starting from the simple intuitive notion of measuring the correlations among the labels the proposed method is designed as a multi label learning method that maximizes the correlations among labels to evaluate its effectiveness the proposed method is compared with the state of the art approaches extensive experiments demonstrated the proposed method to be highly competitive with the other multi label approaches

modeling the shape of the scene a holistic representation of the spatial envelope in this paper we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions the procedure is based on a very low dimensional representation of the scene that we term the spatial envelope we propose a set of perceptual dimensions naturalness openness roughness expansion ruggedness that represent the dominant spatial structure of a scene then we show that these dimensions may be reliably estimated using spectral and coarsely localized information the model generates a multidimensional space in which scenes sharing membership in semantic categories e g streets highways coasts are projected closed together the performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category

frequentist accuracy of bayesian estimates in the absence of relevant prior experience popular bayesian estimation techniques usually begin with some form of uninformative prior distribution intended to have minimal inferential influence bayes rule will still produce nice looking estimates and credible intervals but these lack the logical force attached to experience based priors and require further justification this paper concerns the frequentist assessment of bayes estimates a simple formula is shown to give the frequentist standard deviation of a bayesian point estimate the same simulations required for the point estimate also produce the standard deviation exponential family models make the calculations particularly simple and bring in a connection to the parametric bootstrap

jackknife bootstrap and other resampling methods in regression analysis motivated by a representation for the least squares estimator we propose a class of weighted jackknife variance estimators for the least squares estimator by deleting any fixed number of observations at a time they are unbiased for homoscedastic errors and a special case the delete one jackknife is almost unbiased for heteroscedastic errors the method is extended to cover nonlinear parameters regression m estimators nonlinear regression and generalized linear models interval estimators can be constructed from the jackknife histogram three bootstrap methods are considered two are shown to give biased variance estimators and one does not have the bias robustness property enjoyed by the weighted delete one jackknife a general method for resampling residuals is proposed it gives variance estimators that are bias robust several bias reducing estimators are proposed some simulation results are reported

exemplar based image inpainting using multiscale graph cuts we present a novel formulation of exemplar based inpainting as a global energy optimization problem written in terms of the offset map the proposed energy function combines a data attachment term that ensures the continuity of reconstruction at the boundary of the inpainting domain with a smoothness term that ensures a visually coherent reconstruction inside the hole this formulation is adapted to obtain a global minimum using the graph cuts algorithm to reduce the computational complexity we propose an efficient multiscale graph cuts algorithm to compensate the loss of information at low resolution levels we use a feature representation computed at the original image resolution this permits alleviation of the ambiguity induced by comparing only color information when the image is represented at low resolution levels our experiments show how well the proposed algorithm performs compared with other recent algorithms

dual diversified dynamical gaussian process latent variable model for video repairing in this paper we propose a dual diversified dynamical gaussian process latent variable model text d 3 gplvm to tackle the video repairing issue for preservation purposes videos have to be conserved on media however storing on media such as films and hard disks can suffer from unexpected data loss for instance physical damage so repairing of missing or damaged pixels is essential for better video maintenance most methods seek to fill in missing holes by synthesizing similar textures from local patches the neighboring pixels consecutive frames or the whole video however these can introduce incorrect contexts especially when the missing hole or number of damaged frames is large furthermore simple texture synthesis can introduce artifacts in undamaged and recovered areas to address aforementioned problems we introduce two diversity encouraging priors to both of inducing points and latent variables for considering the variety in existing videos in text d 3 gplvm the inducing points constitute a smaller subset of observed data while latent variables are a low dimensional representation of observed data since they have a strong correlation with the observed data it is essential that both of them can capture distinct aspects of and fully represent the observed data the dual diversity encouraging priors ensure that the trained inducing points and latent variables are more diverse and resistant for context aware and artifacts free based video repairing the defined objective function in our proposed model is initially not analytically tractable and must be solved by variational inference finally experimental testing results illustrate the robustness and effectiveness of our method for damaged video repairing

the mean squared error of small area predictors constructed with estimated area variances in the small area estimation literature the sampling error variances are customarily assumed to be known or to depend on a finite number of parameters we consider the empirical best linear unbiased predictor eblup obtained by using the individual directly estimated variance for each small area an approximation for the mean squared error mse of the eblup that recognizes the impact on the predictors of estimation of the variance components is derived simulation studies show that the theoretical expressions are good approximations for the mse of the predictors unless the between area variance component is very small relative to the within area variance an improved estimator of the mse is developed that has smaller overestimation than the original estimator when the between area variance component is small the robustness of the mse estimator is studied and predictors for nonnormal sampling errors are proposed an example from the national resources inventory that motivated the development of the t

correlated logistic model with elastic net regularization for multilabel image classification in this paper we present correlated logistic corrlog model for multilabel image classification corrlog extends conventional logistic regression model into multilabel cases via explicitly modeling the pairwise correlation between labels in addition we propose to learn the model parameters of corrlog with elastic net regularization which helps exploit the sparsity in feature selection and label correlations and thus further boost the performance of multilabel classification corrlog can be efficiently learned though approximately by regularized maximum pseudo likelihood estimation and it enjoys a satisfying generalization bound that is independent of the number of labels corrlog performs competitively for multilabel image classification on benchmark data sets mulan scene mit outdoor scene pascal voc 2007 and pascal voc 2012 compared with the state of the art multilabel classification algorithms

a james stein type estimator for combining unbiased and possibly biased estimators abstract we present a method for combining unbiased sample data with possibly biased auxiliary information the estimator we derive is similar in spirit to the james stein estimator we prove that the estimator dominates the sample mean under quadratic loss when the auxiliary information is unbiased our estimator has risk slightly greater than the usual combined estimator as the bias increases however the risk of the usual estimator is unbounded while the risk of our estimator is bounded by the risk of the sample mean we show how our estimator can be considered an approximation to the best linear combination of the sample data and the auxiliary information allude to how it can be derived as an empirical bayes estimator and suggest a method for constructing confidence sets finally the performance of our estimator is compared to that of the sample mean and the usual combined estimator using real forestry data

training dcnn by combining max margin max correlation objectives and correntropy loss for multilabel image classification in this paper we build a multilabel image classifier using a general deep convolutional neural network dcnn we propose a novel objective function that consists of three parts i e max margin objective max correlation objective and correntropy loss the max margin objective explicitly enforces that the minimum score of positive labels must be larger than the maximum score of negative labels by a predefined margin which not only improves accuracies of the multilabel classifier but also eases the threshold determination the max correlation objective can make the dcnn model learn a latent semantic space which maximizes the correlations between the feature vectors of the training samples and their corresponding ground truth label vectors projected into this space instead of using the traditional softmax loss we adopt the correntropy loss from the information theory field to minimize the training errors of the dcnn model the proposed framework can be end to end trained comprehensive experimental evaluations on pascal voc 2007 and mir flickr 25k multilabel benchmark data sets with four dcnn models i e alexnet vgg 16 googlenet and resnet demonstrate that the proposed objective function can remarkably improve the performance accuracies of a dcnn model for the task of multilabel image classification

approximations for standard errors of estimators of fixed and random effects in mixed linear models abstract best linear unbiased estimators of the fixed and random effects of mixed linear models are available when the true values of the variance ratios are known if the true values are replaced by estimated values the mean squared errors of the estimators of the fixed and random effects increase in size the magnitude of this increase is investigated and a general approximation is proposed the performance of this approximation is investigated in the context of a the estimation of the effects of the balanced one way random model and b the estimation of treatment contrasts for balanced incomplete block designs

a model for incorporating historical controls into a meta analysis a method is presented for estimating the treatment effect in a meta analysis when some of the studies are comparative studies and the rest are noncomparative historical control studies a random effects model is used in which the baseline effect in each study is random but the treatment effect is constant with this model the appropriate contribution of the historical studies can be determined extensions of the model are developed to accommodate preliminary tests for bias and for the possibility that the treatment effect is heterogeneous

adaptive subspace based inverse projections via division into multiple sub problems for missing image data restoration this paper presents adaptive subspace based inverse projections via division into multiple sub problems asip dimss for missing image data restoration in the proposed method a target problem for estimating missing image data is divided into multiple sub problems and each sub problem is iteratively solved with the constraints of other known image data by projection into a subspace model of image patches the solution of each sub problem is calculated where we call this procedure subspace based inverse projection for simplicity the proposed method can use higher dimensional subspaces for finding unique solutions in each sub problem and successful restoration becomes feasible since a high level of image representation performance can be preserved this is the biggest contribution of this paper furthermore the proposed method generates several subspaces from known training examples and enables derivation of a new criterion in the above framework to adaptively select the optimal subspace for each target patch in this way the proposed method realizes missing image data restoration using asip dims since our method can estimate any kind of missing image data its potential in two image restoration tasks image inpainting and super resolution based on several methods for multivariate analysis is also shown in this paper

a semi bayes approach to the analysis of correlated multiple associations with an application to an occupational cancer mortality study thomas et al presented the application of empirical bayes methods to the problem of multiple inference in epidemiologic studies one limitation of their approach which they noted was the need to assume exchangeable log relative risk parameters and independent relative risk estimates numerical integration was also required here i generalize their approach to allow for non exchangeable parameters and non independent estimates the resulting method is bayesian in so far as some feature of the prior distribution are specified from prior information but is empirical bayes in so far as some explicit parameters in the prior distribution are estimated from the data estimation is based on approximations to the posterior distribution this allows one to implement the approach with standard software packages for matrix algebra the method is illustrated in an occupational mortality study of 84 exposure cancer associations

optimal image alignment with random projections of manifolds algorithm and geometric analysis this paper addresses the problem of image alignment based on random measurements image alignment consists of estimating the relative transformation between a query image and a reference image we consider the specific problem where the query image is provided in compressed form in terms of linear measurements captured by a vision sensor we cast the alignment problem as a manifold distance minimization problem in the linear subspace defined by the measurements the transformation manifold that represents synthesis of shift rotation and isotropic scaling of the reference image can be given in closed form when the reference pattern is sparsely represented over a parametric dictionary we show that the objective function can then be decomposed as the difference of two convex functions dc in the particular case where the dictionary is built on gaussian functions thus the optimization problem becomes a dc program which in turn can be solved globally by a cutting plane method the quality of the solution is typically affected by the number of random measurements and the condition number of the manifold that describes the transformations of the reference image we show that the curvature which is closely related to the condition number remains bounded in our image alignment problem which means that the relative transformation between two images can be determined optimally in a reduced subspace

a unified jackknife theory for empirical best prediction with m estimation the paper presents a unified jackknife theory for a fairly general class of mixed models which includes some of the widely used mixed linear models and generalized linear mixed models as special cases the paper develops jackknife theory for the important but so far neglected prediction problem for the general mixed model for estimation of fixed parameters a jackknife method is considered for a general class of m estimators which includes the maximum likelihood residual maximum likelihood and anova estimators for mixed linear models and the recently developed method of simulated moments estimators for generalized linear mixed models for both the prediction and estimation problems a jackknife method is used to obtain estimators of the mean squared errors mse asymptotic unbiasedness of the mse estimators is shown to hold essentially under certain moment conditions simulation studies undertaken support our theoretical results 1 introduction due to the advent of high speed computers and powerful software computer oriented statistical methods including various resampling methods have received considerable attention in recent years as statisticians are constantly facing complex problems the jackknife method is one such simple resampling method which is very popular among survey samplers primarily due

mathematical models for local nontexture inpaintings dedicated to stanley osher on the occasion of his 60th birthday abstract inspired by the recent work of bertalmio et al on digital inpaintings siggraph 2000 we develop general mathematical models for local inpaintings of nontexture images on smooth regions inpaintings are connected to the harmonic and biharmonic extensions and inpainting orders are analyzed for inpaintings involving the recovery of edges we study a variational model that is closely connected to the classical total variation tv denoising model of rudin osher and fatemi phys d 60 1992 pp 259 268 other models are also discussed based on the mumford shah regularity comm pure appl math xlii 1989 pp 577 685 and curvature driven diffusions cdd of chan and shen j visual comm image rep 12 2001 the broad applications of the inpainting models are demonstrated through restoring scratched old photos disocclusion in vision analysis text removal digital zooming and edge based image coding