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

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

low delay low bit rate speech coder a vector quantizing speech coder includes a short term predictive filter which receives originally sampled speech signal s n and decorrelates it into a residual signal r n a device is provided to quantize the residual signal r n at a low bit rate the device also generates a reconstructed signal r n from which coefficients for adjusting the short term predictive filter are dynamically derived

the crossover phenomena in surface growth models with height dependent noise abstract in this paper we consider several known growth processes with height dependent noise this type of noise is interesting from a theoretical standpoint for example it paves the way to the derivation of the exact height distribution of the kpz equation through the hopf cole transformation in addition it may have implications for experimental growth processes using numerical methods we observe that adding such a noise to different growth processes can change their universality class or ruin the scaling laws in the case of mullins herring equation a two fold cross over is observed

local rademacher complexity for multi label learning we analyze the local rademacher complexity of empirical risk minimization based multi label learning algorithms and in doing so propose a new algorithm for multi label learning rather than using the trace norm to regularize the multi label predictor we instead minimize the tail sum of the singular values of the predictor in multi label learning benefiting from the use of the local rademacher complexity our algorithm therefore has a sharper generalization error bound compared with methods that minimize over all singular values concentrating on the tail singular values results in better recovery of the low rank structure of the multi label predictor which plays an important role in exploiting label correlations we propose a new conditional singular value thresholding algorithm to solve the resulting objective function moreover a variance control strategy is employed to reduce the variance of variables in optimization empirical studies on real world data sets validate our theoretical results and demonstrate the effectiveness of the proposed algorithm for multi label learning

multilabel classification with principal label space transformation we consider a hypercube view to perceive the label space of multilabel classification problems geometrically the view allows us not only to unify many existing multilabel classification approaches but also design a novel algorithm principal label space transformation plst that captures key correlations between labels before learning the simple and efficient plst relies on only singular value decomposition as the key step we derive the theoretical guarantee of plst and evaluate its empirical performance using real world data sets experimental results demonstrate that plst is faster than the traditional binary relevance approach and is superior to the modern compressive sensing approach in terms of both accuracy and efficiency

on large margin hierarchical classification with multiple paths hierarchical classification is critical to knowledge management and exploration as is gene function prediction and document categorization in hierarchical classification an input is classified according to a structured hierarchy in such a situation the central issue is how to effectively utilize the interclass relationship to improve the generalization performance of flat classification ignoring such dependency in this article we propose a novel large margin method through constraints characterizing a multipath hierarchy where class membership can be nonexclusive the proposed method permits a treatment of various losses for hierarchical classification for implementation we focus on the symmetric difference loss and two large margin classifiers support vector machines and learning finally theoretical and numerical analyses are conducted in addition to an application to gene function prediction they suggest that the proposed method achieves the desired objective and outperforms strong comp

incremental algorithms for hierarchical classification we study the problem of classifying data in a given taxonomy when classifications associated with multiple and or partial paths are allowed we introduce a new algorithm that incrementally learns a linear threshold classifier for each node of the taxonomy a hierarchical classification is obtained by evaluating the trained node classifiers in a top down fashion to evaluate classifiers in our multipath framework we define a new hierarchical loss function the h loss capturing the intuition that whenever a classification mistake is made on a node of the taxonomy then no loss should be charged for any additional mistake occurring in the subtree of that node making no assumptions on the mechanism generating the data instances and assuming a linear noise model for the labels we bound the h loss of our on line algorithm in terms of the h loss of a reference classifier knowing the true parameters of the label generating process we show that in expectation the excess cumulative h loss grows at most logarithmically in the length of the data sequence furthermore our analysis reveals the precise dependence of the rate of convergence on the eigenstructure of the data each node observes our theoretical results are complemented by a number of experiments on texual corpora in these experiments we show that after only one epoch of training our algorithm performs much better than perceptron based hierarchical classifiers and reasonably close to a hierarchical support vector machine

predicting multivariate responses in multiple linear regression we look at the problem of predicting several response variables from the same set of explanatory variables the question is how to take advantage of correlations between the response variables to improve predictive accuracy compared with the usual procedure of doing individual regressions of each response variable on the common set of predictor variables a new procedure is introduced called the curds and whey method its use can substantially reduce prediction errors when there are correlations between responses while maintaining accuracy even if the responses are uncorrelated in extensive simulations the new procedure is compared with several previously proposed methods for predicting multiple responses including partial least squares and exhibits superior accuracy one version can be easily implemented in the context of standard statistical packages

mandatory leaf node prediction in hierarchical multilabel classification in hierarchical classification the output labels reside on a tree or directed acyclic graph dag structured hierarchy on testing the prediction paths of a given test example may be required to end at leaf nodes of the label hierarchy this is called mandatory leaf node prediction mlnp and is particularly useful when the leaf nodes have much stronger semantic meaning than the internal nodes however while there have been a lot of mlnp methods in hierarchical multiclass classification performing mlnp in hierarchical multilabel classification is difficult in this paper we propose novel mlnp algorithms that consider the global label hierarchy structure we show that the joint posterior probability over all the node labels can be efficiently maximized by dynamic programming for label trees or greedy algorithm for label dags in addition both algorithms can be further extended for the minimization of the expected symmetric loss experiments are performed on real world mlnp data sets with label trees and label dags the proposed method consistently outperforms other hierarchical and flat multilabel classification methods

surface and bulk properties of ballistic deposition models with bond breaking abstract we introduce a new class of growth models with a surface restructuring mechanism in which impinging particles may dislodge suspended particles previously aggregated on the same column in the deposit the flux of these particles is controlled through a probability p these systems present a crossover for small values of p from random to correlated kpz growth of surface roughness which is studied through scaling arguments and monte carlo simulations on one and two dimensional substrates we show that the crossover characteristic time t scales with p according to t p y with y n 1 and that the interface width at saturation w s a t scales as w s a t p with n 1 2 where n is either the maximal number of broken bonds or of dislodged suspended particles this result shows that the sets of exponents y 1 and 1 2 or y 2 and 1 found in all previous works focusing on systems with this same type of crossover are not universal using scaling arguments we show that the bulk porosity p of the deposits scales as p p y for small values of p this general scaling relation is confirmed by our numerical simulations and explains previous results present in literature

biologically inspired feature manifold for scene classification biologically inspired feature bif and its variations have been demonstrated to be effective and efficient for scene classification it is unreasonable to measure the dissimilarity between two bifs based on their euclidean distance this is because bifs are extrinsically very high dimensional and intrinsically low dimensional i e bifs are sampled from a low dimensional manifold and embedded in a high dimensional space therefore it is essential to find the intrinsic structure of a set of bifs obtain a suitable mapping to implement the dimensionality reduction and measure the dissimilarity between two bifs in the low dimensional space based on their euclidean distance in this paper we study the manifold constructed by a set of bifs utilized for scene classification form a new dimensionality reduction algorithm by preserving both the geometry of intra bifs and the discriminative information inter bifs termed discriminative and geometry preserving projections dgpp and construct a new framework for scene classification in this framework we represent an image based on a new bif which combines the intensity channel the color channel and the c1 unit of a color image then we project the high dimensional bif to a low dimensional space based on dgpp and finally we conduct the classification based on the multiclass support vector machine svm thorough empirical studies based on the usc scene dataset demonstrate that the proposed framework improves the classification rates around 100 relatively and the training speed 60 times for different sites in comparing with previous gist proposed by siagian and itti in 2007

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

multicategory ψ learning in binary classification margin based techniques usually deliver high performance as a result a multicategory problem is often treated as a sequence of binary classifications in the absence of a dominating class this treatment may be suboptimal and may yield poor performance such as for support vector machines svms we propose a novel multicategory generalization of learning that treats all classes simultaneously the new generalization eliminates this potential problem while at the same time retaining the desirable properties of its binary counterpart we develop a statistical learning theory for the proposed methodology and obtain fast convergence rates for both linear and nonlinear learning examples we demonstrate the operational characteristics of this method through a simulation our results indicate that the proposed methodology can deliver accurate class prediction and is more robust against extreme observations than its svm counterpart

asymptotic generalization bound of fisher s linear discriminant analysis fisher s linear discriminant analysis flda is an important dimension reduction method in statistical pattern recognition it has been shown that flda is asymptotically bayes optimal under the homoscedastic gaussian assumption however this classical result has the following two major limitations 1 it holds only for a fixed dimensionality d and thus does not apply when d and the training sample size n are proportionally large 2 it does not provide a quantitative description on how the generalization ability of flda is affected by d and n in this paper we present an asymptotic generalization analysis of flda based on random matrix theory in a setting where both d and n increase and d n 0 1 the obtained lower bound of the generalization discrimination power overcomes both limitations of the classical result i e it is applicable when d and n are proportionally large and provides a quantitative description of the generalization ability of flda in terms of the ratio d n and the population discrimination power besides the discrimination power bound also leads to an upper bound on the generalization error of binary classification with flda

a discrete method to study stochastic growth equations a cellular automata perspective using cellular automata dynamics a discrete technique to study stochastic growth equations sge is presented by analogy to deposition models in which the growth rule depends on height differences between neighbours we introduce an interface growth process with synchronous updating in which the transition probability for a given site i to receive a particle at a time t is defined as pi t exp i t and are the model parameters and i t is a function which depends on the height of the site i and its neighbours and its functional form is specified through discretization of the deterministic part of the growth equation associated with a given deposition process to validate the method we study its application to two linear sge the edwards wilkinson equation and the mullins herring equation and a nonlinear one the kardar parisi zhang equation the statistical analysis of the height distributions in simulations recovered the correct values for roughening exponents confirming that the processes generated are indeed in the universality classes of the original growth equations we also observed a crossover from random deposition to the correlated regime when the parameter is varied in each case studied

digital speech coder having improved vector excitation source an improved excitation vector generation and search technique fig 1 is described for a code excited linear prediction celp speech coder 100 using a codebook of excitation code vectors a set of m basis vectors vm n are used along with the excitation signal codewords i to generate the codebook of excitation vectors ui n according to a vector sum technique 120 of converting the selector codewords into a plurality of interim data signals multiplying the set of m basis vectors by the interim data signals and summing the resultant vectors to produce the set of 2m codebook vectors the entire codebook of 2m possible excitation vectors is efficiently searched by using the vector sum generation technique with the m basis vectors without ever having to generate and evaluate each of the 2m code vectors themselves furthermore only m basis vectors need to be stored in memory 114 as opposed to all 2m code vectors

on multilabel classification and ranking with bandit feedback we present a novel multilabel ranking algorithm working in partial information settings the algorithm is based on 2nd order descent methods and relies on upper confidence bounds to trade off exploration and exploitation we analyze this algorithm in a partial adversarial setting where covariates can be adversarial but multilabel probabilities are ruled by generalized linear models we show o t1 2 log t regret bounds which improve in several ways on the existing results we test the effectiveness of our upper confidence scheme by contrasting against full information baselines on diverse real world multilabel data sets often obtaining comparable performance

universal behavior of the coefficients of the continuous equation in competitive growth models the competitive growth models cgm involving only one kind of particles are a mixture of two processes one with probability p and the other with probability 1 p the p dependence produce crossovers between two different regimes we demonstrate that the coefficients of the continuous equation describing their universality classes are quadratic in p or 1 p we show that the origin of such dependence is the existence of two different average time rates thus the quadratic p dependence is a universal behavior of all the cgm we derive analytically the continuous equations for two cgm in 1 1 dimensions from the microscopic rules using a regularization procedure we propose generalized scalings that reproduce the scaling behavior in each regime in order to verify the analytic results and the scalings we perform numerical integrations of the derived analytical equations the results are in excellent agreement with those of the microscopic cgm presented here and with the proposed scalings

pulse pattern excited linear prediction voice coder speech coding of the code excited linear predictive type is implemented by providing an excitation vector which comprises a set of a pre determined number of pulse patterns from a codebook of p pulse patterns which have a selected orientation and a pre determined delay with respect to the starting point of the excitation vector this requires modest computational power and a small memory space which allows it to be implemented in one signal processor

height distributions in competitive one dimensional kardar parisi zhang systems we study the competitive rsos bd model focusing on the validity of the kardar parisi zhang kpz ansatz h t v ensuremath infty t ensuremath gamma t ensuremath beta ensuremath chi and the universality of the height distributions hds near the point where the model has edwards wilkinson ew scaling using numerical simulations for long times we show that the system is asymptotically kpz as expected for values of the probability of the rsos component very close to p p c ensuremath approx 0 83 namely the growth exponents converge to ensuremath beta mathrm kpz 1 3 and the hds converge to the goe tracy widom distribution however the convergence seems to be faster in the last ones while the ew kpz crossover appears in the roughness scaling in a broad range of probabilities p around p c a gaussian goe crossover is not observed in the hds into the same interval possibly being restricted to values of p very close to p c these results improve recent ones reported by de assis et al phys rev e 86 051607 2012 where based on smaller simulations the kpz scaling was argued to break down in a broad range of p around p c

double mode long term prediction in speech coding a method of coding a sampled speech signal vector in an analysis by synthesis coding procedure includes the step of forming an optimum excitation vector comprising a linear combination of a code vector from a fixed code book and a long term predictor vector a first estimate of the long term predictor vector is formed in an open loop analysis a second estimate of the long term predictor vector is formed in a closed loop analysis finally each of the first and second estimates are combined in an exhaustive search with each code vector of the fixed code book to form that excitation vector that gives the best coding of the speech signal vector

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

nonuniversal effects in mixing correlated growth processes with randomness interplay between bulk morphology and surface roughening to construct continuum stochastic growth equations for competitive nonequilibrium surface growth processes of the type rd x that mixes random deposition rd with a correlated growth process x we use a simplex decomposition of the height field a distinction between growth processes x that do and do not create voids in the bulk leads to the definition of the effective probability p eff of the process x that is a measurable property of the bulk morphology and depends on the activation probability p of x in the competitive process rd x the bulk morphology is reflected in the surface roughening via nonuniversal prefactors in the universal scaling of the surface width that scales in p eff the equation and the resulting scaling are derived for x in either a kardar parisi zhang or edwards wilkinson universality class in 1 1 dimensions and are illustrated by an example of x being a ballistic deposition we obtain full data collapse on its corresponding universal scaling function for all p 0 1 we outline the generalizations to 1 n dimensions and to many component competitive growth processes

digital speech coder with baseband residual coding a digital speech coder of the baseband relp type residual excited linear prediction comprises a transmitter 1 having an lpc anaylser 10 a first adaptive inverse filter 11 a decimation lowpass filter 26 for selecting the baseband prediction residue and an encoding and multiplexing circuit 17 and a receiver 2 having a demultiplexing and decoding circuit 21 an interpolator 27 and a first adaptive synthesizing filter 14 the occurrence of tonal noises due to the spectral folding in interpolator 27 is effectively counteracted by arranging prior to the decimation lowpass filter 26 in the transmitter 1 a second adaptive inverse filter 28 which with the aid of an autocorrelator 31 removes possible periodicy from the speech band residue and by including subsequent to the interpolator 27 in the receiver 2 a corresponding second adaptive synthesis filter 32 which reintroduces the desired periodicity in the excitation signal

multi view intact space learning it is practical to assume that an individual view is unlikely to be sufficient for effective multi view learning therefore integration of multi view information is both valuable and necessary in this paper we propose the multi view intact space learning misl algorithm which integrates the encoded complementary information in multiple views to discover a latent intact representation of the data even though each view on its own is insufficient we show theoretically that by combing multiple views we can obtain abundant information for latent intact space learning employing the cauchy loss a technique used in statistical learning as the error measurement strengthens robustness to outliers we propose a new definition of multi view stability and then derive the generalization error bound based on multi view stability and rademacher complexity and show that the complementarity between multiple views is beneficial for the stability and generalization misl is efficiently optimized using a novel iteratively reweight residuals irr technique whose convergence is theoretically analyzed experiments on synthetic data and real world datasets demonstrate that misl is an effective and promising algorithm for practical applications

low bit rate voice coding method and system this low bit rate voice encoding involves short term predictive filtering the voice signal s n using partial correlation related coefficients derived from pre emphasized s n and deriving a short term signal r n then deriving a long term residual signal e n by subtracting a delayed synthesized short term b r n 31 m from said r n and code excited encoding e n into codeword references k s and associated gains g s

new support vector algorithms we propose a new class of support vector algorithms for regression and classification in these algorithms a parameter lets one effectively control the number of support vectors while this can be useful in its own right the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm the accuracy parameter epsilon in the regression case and the regularization constant c in the classification case we describe the algorithms give some theoretical results concerning the meaning and the choice of and report experimental results

stability and generalization we define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds based on the empirical error and the leave one out error the methods we use can be applied in the regression framework as well as in the classification one when the classifier is obtained by thresholding a real valued function we study the stability properties of large classes of learning algorithms such as regularization based algorithms in particular we focus on hilbert space regularization and kullback leibler regularization we demonstrate how to apply the results to svm for regression and classification

classifying imbalanced data sets using similarity based hierarchical decomposition classification of data is difficult if the data is imbalanced and classes are overlapping in recent years more research has started to focus on classification of imbalanced data since real world data is often skewed traditional methods are more successful with classifying the class that has the most samples majority class compared to the other classes minority classes for the classification of imbalanced data sets different methods are available although each has some advantages and shortcomings in this study we propose a new hierarchical decomposition method for imbalanced data sets which is different from previously proposed solutions to the class imbalance problem additionally it does not require any data pre processing step as many other solutions need the new method is based on clustering and outlier detection the hierarchy is constructed using the similarity of labeled data subsets at each level of the hierarchy with different levels being built by different data and feature subsets clustering is used to partition the data while outlier detection is utilized to detect minority class samples the comparison of the proposed method with state of art the methods using 20 public imbalanced data sets and 181 synthetic data sets showed that the proposed method s classification performance is better than the state of art methods it is especially successful if the minority class is sparser than the majority class it has accurate performance even when classes have sub varieties and minority and majority classes are overlapping moreover its performance is also good when the class imbalance ratio is low i e classes are more imbalanced a novel method for imbalanced dataset classification a new hierarchical classifier which does not use a fixed feature class hierarchy uses clustering and outlier detection to construct the hierarchy shows that different feature spaces can be used to build a hierarchy successful when the class imbalanced ratio is low classes are highly overlapping

high dimensional ising model selection using l1 regularized logistic regression we consider the problem of estimating the graph associated with a binary ising markov random field we describe a method based on l1 regularized logistic regression in which the neighborhood of any given node is estimated by performing logistic regression subject to an l1 constraint the method is analyzed under high dimensional scaling in which both the number of nodes p and maximum neighborhood size d are allowed to grow as a function of the number of observations n our main results provide sufficient conditions on the triple n p d and the model parameters for the method to succeed in consistently estimating the neighborhood of every node in the graph simultaneously with coherence conditions imposed on the population fisher information matrix we prove that consistent neighborhood selection can be obtained for sample sizes n d3log p with exponentially decaying error when these same conditions are imposed directly on the sample matrices we show that a reduced sample size of n d2log p suffices for the method to estimate neighborhoods consistently although this paper focuses on the binary graphical models we indicate how a generalization of the method of the paper would apply to general discrete markov random fields

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

shape matching and object recognition using shape contexts we present a novel approach to measuring similarity between shapes and exploit it for object recognition in our framework the measurement of similarity is preceded by 1 solving for correspondences between points on the two shapes 2 using the correspondences to estimate an aligning transform in order to solve the correspondence problem we attach a descriptor the shape context to each point the shape context at a reference point captures the distribution of the remaining points relative to it thus offering a globally discriminative characterization corresponding points on two similar shapes will have similar shape contexts enabling us to solve for correspondences as an optimal assignment problem given the point correspondences we estimate the transformation that best aligns the two shapes regularized thin plate splines provide a flexible class of transformation maps for this purpose the dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points together with a term measuring the magnitude of the aligning transform we treat recognition in a nearest neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image results are presented for silhouettes trademarks handwritten digits and the coil data set

multi label image categorization with sparse factor representation the goal of multilabel classification is to reveal the underlying label correlations to boost the accuracy of classification tasks most of the existing multilabel classifiers attempt to exhaustively explore dependency between correlated labels it increases the risk of involving unnecessary label dependencies which are detrimental to classification performance actually not all the label correlations are indispensable to multilabel model negligible or fragile label correlations cannot be generalized well to the testing data especially if there exists label correlation discrepancy between training and testing sets to minimize such negative effect in the multilabel model we propose to learn a sparse structure of label dependency the underlying philosophy is that as long as the multilabel dependency cannot be well explained the principle of parsimony should be applied to the modeling process of the label correlations the obtained sparse label dependency structure discards the outlying correlations between labels which makes the learned model more generalizable to future samples experiments on real world data sets show the competitive results compared with existing algorithms

linear prediction speech coding with high frequency preemphasis in a speech encoder high frequency components of input digital speech samples are emphasized by a preemphasis filter 11 from the preemphasized samples a spectral parameter ai is derived at frame intervals the input digital samples are weighted by a weighting filter 13 according to a characteristic that is inverse to the characteristic of the preemphasis filter 11 and is a function of the spectral parameter aj a codebook 18 19 is searched for an optimum fricative value in response to a pitch parameter that is derived by an adaptive codebook 16 from a previous fricative value v n and a difference between the weighted speech samples and synthesized speech samples which are in turn derived from past pitch parameters and optimum fricative values whereby the difference is reduced to a minimum index signals representing the spectral parameter pitch parameter and optimum fricative value are multiplexed into a single data stream

large margin semi supervised learning in classification semi supervised learning occurs when a large amount of unlabeled data is available with only a small number of labeled data in such a situation how to enhance predictability of classification through unlabeled data is the focus in this article we introduce a novel large margin semi supervised learning methodology using grouping information from unlabeled data together with the concept of margins in a form of regularization controlling the interplay between labeled and unlabeled data based on this methodology we develop two specific machines involving support vector machines and learning denoted as ssvm and spsi through difference convex programming in addition we estimate the generalization error using both labeled and unlabeled data for tuning regularizers finally our theoretical and numerical analyses indicate that the proposed methodology achieves the desired objective of delivering high performance in generalization particularly against some strong performers

coarse grained approach for universality classification of discrete models discrete and continuous models belonging to a universality class share the same linearities and or nonlinearities in this work we propose a new approach to calculate coarse grained coefficients of the continuous differential equation from discrete models we apply small constant translations in a test space and show how to obtain these coefficients from the transformed average interface growth velocity using the examples of the ballistic deposition bd model and the restricted solid on solid rsos model both belonging to the kardar parisi zhang kpz universality class we demonstrate how to apply our approach to calculate analytically the corresponding coefficients of the kpz equation our analytical nonlinear coefficients are in agreement with numerical results obtained by monte carlo tilted simulations in addition to the bd and the rsos we study a competitive rsos model that shows crossover between the kpz and edwards wilkinson universality classes

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

langevin equations for competitive growth models langevin equations for several competitive growth models in one dimension are derived for models with crossover from random deposition rd to some correlated deposition cd dynamics with small probability p of cd the surface tension and the nonlinear coefficient of the associated equations have linear dependence on p due solely to this random choice however they also depend on the regularized step functions present in the analytical representations of the cd whose expansion coefficients scale with p according to the divergence of local height differences when p 0 the superposition of those scaling factors gives p 2 for random deposition with surface relaxation rdsr as the cd and p p 3 2 for ballistic deposition bd as the cd in agreement with simulation and other scaling approaches for bidisperse ballistic deposition bbd the same scaling of rd bd model is found the langevin equation for the model with competing rdsr and bd with probability p for the latter is also constructed it shows linear p dependence of while the quadratic dependence observed in previous simulations is explained by an additional crossover before the asymptotic regime the results highlight the relevance of scaling of the coefficients of step function expansions in systems with steep surfaces which is responsible for noninteger exponents in some p dependent stochastic equations and the importance of the physical correspondence of aggregation rules and equation coefficients

speech signal coding using correlation valves between subframes a speech signal coding system for coding a speech signal at a bit rate of 8 to 4 kb s wherein the amount of calculation for fractional search of delays of an adaptive codebook is reduced significantly before a fractional delay of the adaptive codebook is found candidates of integer delay are found by an open loop using correlation values a search for a fractional delay by a closed loop is performed for a search range for fractional delays which is provided by several samples of each integer delay candidate thus found using the correlation values the fractional delay search is realized by polyphase filtering of an excitation signal in the past in the search a plurality of candidates of fractional delay may be found for each integer delay candidate from the adaptive codebook in this instance a fractional delay is determined decisively from the decimal delay candidates after a search of an excitation codebook

algorithm dependent generalization bounds for multi task learning often tasks are collected for multi task learning mtl because they share similar feature structures based on this observation in this paper we present novel algorithm dependent generalization bounds for mtl by exploiting the notion of algorithmic stability we focus on the performance of one particular task and the average performance over multiple tasks by analyzing the generalization ability of a common parameter that is shared in mtl when focusing on one particular task with the help of a mild assumption on the feature structures we interpret the function of the other tasks as a regularizer that produces a specific inductive bias the algorithm for learning the common parameter as well as the predictor is thereby uniformly stable with respect to the domain of the particular task and has a generalization bound with a fast convergence rate of order mathcal o 1 n where n is the sample size of the particular task when focusing on the average performance over multiple tasks we prove that a similar inductive bias exists under certain conditions on the feature structures thus the corresponding algorithm for learning the common parameter is also uniformly stable with respect to the domains of the multiple tasks and its generalization bound is of the order mathcal o 1 t where t is the number of tasks these theoretical analyses naturally show that the similarity of feature structures in mtl will lead to specific regularizations for predicting which enables the learning algorithms to generalize fast and correctly from a few examples

comb filter speech coding with preselected excitation code vectors in a code excited speech encoder an input speech signal is segmented into speech samples at first intervals and a spectral parameter is derived from the speech samples that occur at second intervals longer than the first intervals the spectral parameter representing the characteristic spectral feature each speech sample is weighted with the spectral parameter for producing weighted speech samples the pitch period of the speech signal is determined from the weighted speech samples a predetermined number of excitation code vectors having smaller amounts of distortion are selected from excitation codebooks as candidate code vectors the candidate vectors are comb filtered with a delay time set equal to the pitch period one of the filtered code vectors having a minimum distortion is selected the selected filtered code vector is calculated for minimum distortion and in response thereto a gain code vector is selected from a gain codebook index signals representing the spectral parameter the pitch period the selected excitation and gain code vectors are multiplexed for transmission or storage

on ψ learning the concept of large margins have been recognized as an important principle in analyzing learning methodologies including boosting neural networks and support vector machines svms however this concept alone is not adequate for learning in nonseparable cases we propose a learning methodology called learning that is derived from a direct consideration of generalization errors we provide a theory for learning and show that it essentially attains the optimal rates of convergence in two learning examples finally results from simulation studies and from breast cancer classification confirm the ability of learning to outperform svm in generalization

digital speech coder having improved long term lag parameter determination a method and apparatus is provided for determining the lag of a long term filter in a code excited linear prediction speech coder an open loop lag is first determined using an autocorrelation function the open loop lag is then utilized to generate a limited range over which a closed loop search is performed the range for appropriate values includes lags that are harmonically related to the open loop lag as well as adjacent lags

parameter free scaling relation for nonequilibrium growth processes we discuss a parameter free scaling relation that yields a complete data collapse for large classes of nonequilibrium growth processes we illustrate the power of this scaling relation through various growth models such as the competitive growth model with random deposition and random deposition with surface diffusion or the restricted solid on solid model with different nearest neighbor height differences as well as through a deposition model with temperature dependent diffusion the scaling relation is compared to the familiar family vicsek relation and the limitations of the latter are highlighted

depth first algebraic codebook search for fast coding of speech a codebook is searched in view of encoding a sound signal this codebook consists of a set of codevectors each of 40 positions and comprising n non zero amplitude pulses assignable to predetermined valid positions to reduce the search complexity a depth first search is used which involves a tree structure with levels ordered from 1 through m a path building operation takes place at each level whereby a candidate path from the previous level is extended by choosing a predetermined number of new pulses and selecting valid positions for said new pulses in accordance with a given pulse order rule and a given selection criterion a path originated at the first level and extended by the path building operations of subsequent levels determines the respective positions of the n non zero amplitude pulse of a candidate codevector use of a signal based pulse position likelihood estimate during the first few levels enable initial pulse screening to start the search on favorable conditions a selection criterion based on maximizing a ratio is used to assess the progress and to choose the best one among competing candidate codevectors

random sieve likelihood and general regression models abstract consider a semiparametric regression model y f x where f is a known function is an unknown vector consists of a random error and possibly of some unobserved variables and the distribution f of x is unspecified this article introduces in a general setting new methodology for estimating and f the proposed method constructs a profile likelihood defined on random level sets a random sieve the proposed method is related to empirical likelihood but is more generally applicable four examples are discussed including a quadratic model high dimensional semiparametric regression a nonparametric random effects model and linear regression with right censored data simulation results and asymptotic analysis support the utility and effectiveness of the proposed method

labelling strategies for hierarchical multi label classification techniques many hierarchical multi label classification systems predict a real valued score for every instance class couple with a higher score reflecting more confidence that the instance belongs to that class these classifiers leave the conversion of these scores to an actual label set to the user who applies a cut off value to the scores the predictive performance of these classifiers is usually evaluated using threshold independent measures like precision recall curves however several applications require actual label sets and thus an automatic labelling strategy in this paper we present and evaluate different alternatives to perform the actual labelling in hierarchical multi label classification we investigate the selection of both single and multiple thresholds despite the existence of multiple threshold selection strategies in non hierarchical multi label classification they cannot be applied directly to the hierarchical context the proposed strategies are implemented within two main approaches optimisation of a certain performance measure of interest such as f measure or hierarchical loss and simulating training set properties such as class distribution or label cardinality in the predictions we assess the performance of the proposed labelling schemes on 10 datasets from different application domains our results show that selecting multiple thresholds may result in an efficient and effective solution for hierarchical multi label problems highlightswe select single or multiple thresholds for hierarchical multi label classifiers selecting multiple thresholds often yield better label sets in lesser time we show that optimising h loss tends to favor empty label sets multiple threshold selection is preferred for micro f measure and hmc loss imitating training set properties is a competitive approach to optimise hmc loss

coarse grained approach for volume conserving models abstract volume conserving surface vcs models without deposition and evaporation as well as ideal molecular beam epitaxy models are prototypes to study the symmetries of conserved dynamics in this work we study two similar vcs models with conserved noise which differ from each other by the axial symmetry of their dynamic hopping rules we use a coarse grained approach to analyze the models and show how to determine the coefficients of their corresponding continuous stochastic differential equation sde within the same universality class the employed method makes use of small translations in a test space which contains the stationary probability density function spdf in case of the symmetric model we calculate all the coarse grained coefficients of the related conserved kardar parisi zhang kpz equation with respect to the symmetric model the asymmetric model adds new terms which have to be analyzed first of all the diffusion term whose coarse grained coefficient can be determined by the same method in contrast to other methods the used formalism allows to calculate all coefficients of the sde theoretically and within limits numerically above all the used approach connects the coefficients of the sde with the spdf and hence gives them a precise physical meaning