on the effectiveness of whole spectral shape for vowel perception the formant hypothesis of vowel perception where the lowest two or three formant frequencies are essential cues for vowel quality perception is widely accepted there has however been some controversy suggesting that formant frequencies are not sufficient and that the whole spectral shape is necessary for perception three psychophysical experiments were performed to study this question in the first experiment the first or second formant peak of stimuli was suppressed as much as possible while still maintaining the original spectral shape the responses to these stimuli were not radically different from the ones for the unsuppressed control in the second experiment f2 suppressed stimuli whose amplitude ratios of high to low frequency components were systemically changed were used the results indicate that the ratio changes can affect perceived vowel quality especially its place of articulation in the third experiment the full formant stimuli whose amplitude ratios were changed from the original and whose f2 s were kept constant were used the results suggest that the amplitude ratio is equal to or more effective than f2 as a cue for place of articulation we conclude that formant frequencies are not exclusive cues and that the whole spectral shape can be crucial for vowel perception

some effects of duration on vowel recognition this study was designed to examine the role of duration in vowel perception by testing listeners on the identification of cvc syllables generated at different durations test signals consisted of synthesized versions of 300 utterances selected from a large multitalker database of hvd syllables hillenbrand et al j acoust soc am 97 3099 3111 1995 four versions of each utterance were synthesized 1 an original duration set vowel duration matched to the original utterance 2 a neutral duration set duration fixed at 272 ms the grand mean across all vowels 3 a short duration set duration fixed at 144 ms two standard deviations below the mean and 4 a long duration set duration fixed at 400 ms two standard deviations above the mean experiment 1 used a formant synthesizer while a second experiment was an exact replication using a sinusoidal synthesis method that represented the original vowel spectrum more precisely than the formant synthesizer findings included 1 duration

estimation of the mean of a multivariate normal distribution estimation of the means of independent normal random variables is considered using sum of squared errors as loss an unbiased estimate of risk is obtained for an arbitrary estimate and certain special classes of estimates are then discussed the results are applied to smoothing by use of moving averages and to trimmed analogs of the james stein estimate a suggestion is made for calculating approximate confidence sets for the mean vector centered at an arbitrary estimate

speech perception based on spectral peaks versus spectral shape this study was designed to measure the relative contributions to speech intelligibility of spectral envelope peaks including but not limited to formants versus the detailed shape of the spectral envelope the problem was addressed by asking listeners to identify sentences and nonsense syllables that were generated by two structurally identical source filter synthesizers one of which constructs the filter function based on the detailed spectral envelope shape while the other constructs the filter function using a purposely coarse estimate that is based entirely on the distribution of peaks in the envelope viewed in the broadest terms the results showed that nearly as much speech information is conveyed by the peaks only method as by the detail preserving method just as clearly however every test showed some measurable advantage for spectral detail although the differences were not large in absolute terms

towards a generalized singular value decomposition we suggest a form for and give a constructive derivation of the generalized singular value decomposition of any two matrices having the same number of columns we outline its desirable characteristics and compare it to an earlier suggestion by van loan siam j numer anal 13 1976 pp 76 83 the present form largely follows from the work of van loan but is slightly more general and computationally more amenable than that in the paper cited we also prove a useful extension of a theorem of stewart siam rev 19 1977 pp 634 662 on unitary decompositions of submatrices of a unitary matrix

a narrow band pattern matching model of vowel perception the purpose of this paper is to propose and evaluate a new model of vowel perception which assumes that vowel identity is recognized by a template matching process involving the comparison of narrow band input spectra with a set of smoothed spectral shape templates that are learned through ordinary exposure to speech in the present simulation of this process the input spectra are computed over a sufficiently long window to resolve individual harmonics of voiced speech prior to template creation and pattern matching the narrow band spectra are amplitude equalized by a spectrum level normalization process and the information bearing spectral peaks are enhanced by a flooring procedure that zeroes out spectral values below a threshold function consisting of a center weighted running average of spectral amplitudes templates for each vowel category are created simply by averaging the narrow band spectra of like vowels spoken by a panel of talkers in the present implementation separate templates are used for men women and children the pattern matching is implemented with a simple city block distance measure given by the sum of the channel by channel differences between the narrow band input spectrum level equalized and floored and each vowel template spectral movement is taken into account by computing the distance measure at several points throughout the course of the vowel the input spectrum is assigned to the vowel template that results in the smallest difference accumulated over the sequence of spectral slices the model was evaluated using a large database consisting of 12 vowels in hvd context spoken by 45 men 48 women and 46 children the narrow band model classified vowels in this database with a degree of accuracy 91 4 approaching that of human listeners

the discrimination subspace model abstract for a p variate normal random vector measured in two populations we propose a method of discrimination under the constraint that all differences between the two populations occur in a subspace of dimension q p this method of classification is based on the discrimination subspace model denoted by dsm q and is intermediate between linear and quadratic discrimination it combines the ideas of dimension reduction and constraints on the parameter space thus substantially reducing the number of parameters to be estimated the maximum likelihood estimators of the model are presented and the performance of dsm q versus quadratic and linear discrimination is assessed via simulation it is generally shown that discrimination based on dsm q consistently yields noticeably lower expected actual error rates relative to the traditional methods the method is illustrated with a real data example and is compared to linear and quadratic discrimination using a leave one out method the example confirms t

sparse orthogonal linear discriminant analysis in this paper sparse orthogonal linear discriminant analysis olda is studied the main contributions of the present work include the following i all minimum frobenius norm dimension solutions of the optimization problem used for establishing olda are characterized explicitly and ii this explicit characterization leads to two numerical algorithms for computing a sparse linear transformation for olda the first is based on the gradient flow approach while the second is a sequential linear bregman method we experiment with real world datasets to illustrate that the sequential linear bregman method is much better than the gradient flow approach the sequential linear bregman method always achieves comparable classification accuracy with the normal olda satisfactory sparsity and orthogonality and acceptable cpu times

evaluating models of vowel perception there is a long standing debate concerning the efficacy of formant based versus whole spectrum models of vowel perception categorization data for a set of synthetic steady state vowels were used to evaluate both types of models the models tested included various combinations of formant frequencies and amplitudes principal components derived from excitation patterns and perceptually scaled lpc cepstral coefficients the stimuli were 54 five formant synthesized vowels that had a common f1 frequency and varied orthogonally in f2 and f3 frequency twelve speakers of american english categorized the stimuli as the vowels or results indicate that formant frequencies provided the best account of the data only if nonlinear terms in the form of squares and cross products of the formant values were also included in the analysis the excitation pattern principal components also produced reasonably accurate fits to the data although a wish to use the lowest dimensional representation would dicta

the use of acoustic cues for phonetic identification effects of spectral degradation and electric hearing although some cochlear implant ci listeners can show good word recognition accuracy it is not clear how they perceive and use the various acoustic cues that contribute to phonetic perceptions in this study the use of acoustic cues was assessed for normal hearing nh listeners in optimal and spectrally degraded conditions and also for ci listeners two experiments tested the tense lax vowel contrast varying in formant structure vowel inherent spectral change and vowel duration and the word final fricative voicing contrast varying in f1 transition vowel duration consonant duration and consonant voicing identification results were modeled using mixed effects logistic regression these experiments suggested that under spectrally degraded conditions nh listeners decrease their use of formant cues and increase their use of durational cues compared to nh listeners ci listeners showed decreased use of spectral cues like formant structure and formant change and consonant voicing and showed greater use of durational cues especially for the fricative contrast the results suggest that although nh and ci listeners may show similar accuracy on basic tests of word phoneme or feature recognition they may be using different perceptual strategies in the process

non linear elliptic and parabolic equations involving measure data in this paper we prove the existence of solutions for equations of the type div a du f in a bounded open set u 0 on where a is a possibly non linear function satisfying some coerciveness and monotonicity assumptions and f is a bounded measure we also consider the equation div a du g u f in u 0 on with f l1 or f m u u 0 and the parabolic equivalent of the first elliptic equation

regularized discriminant analysis abstract linear and quadratic discriminant analysis are considered in the small sample high dimensional setting alternatives to the usual maximum likelihood plug in estimates for the covariance matrices are proposed these alternatives are characterized by two parameters the values of which are customized to individual situations by jointly minimizing a sample based estimate of future misclassification risk computationally fast implementations are presented and the efficacy of the approach is examined through simulation studies and application to data these studies indicate that in many circumstances dramatic gains in classification accuracy can be achieved

complexity of acoustic production based models of speech perception discriminant analysis models trained on acoustic vowel production data have been found to have significant correlation with listeners perception two regularised discriminant analysis models were trained on monolingual speakers vowels one model was trained on north central peninsular spanish vowel tokens and the other on western canadian english vowel tokens for each language the model which resulted in the lowest cross validated classification error rate was close to the least complex model possible i e close to linear discriminant analysis using the variances of each acoustic variable but not the covariances between variables in order to make cross language vowel perception predictions the spanish model was used to classify english vowel tokens and the english model was used to classify spanish vowel tokens results suggest that monolingual north central peninsular spanish listeners would assimilate most tokens of western canadian english i and i to spanish i and e respectively and thu

model sparsity and brain pattern interpretation of classification models in neuroimaging interest is increasing in applying discriminative multivariate analysis techniques to the analysis of functional neuroimaging data model interpretation is of great importance in the neuroimaging context and is conventionally based on a brain map derived from the classification model in this study we focus on the relative influence of model regularization parameter choices on both the model generalization the reliability of the spatial patterns extracted from the classification model and the ability of the resulting model to identify relevant brain networks defining the underlying neural encoding of the experiment for a support vector machine logistic regression and fisher s discriminant analysis we demonstrate that selection of model regularization parameters has a strong but consistent impact on the generalizability and both the reproducibility and interpretable sparsity of the models for both 2 and 1 regularization importantly we illustrate a trade off between model spatial reproducibility and prediction accuracy we show that known parts of brain networks can be overlooked in pursuing maximization of classification accuracy alone with either 2 and or 1 regularization this supports the view that the quality of spatial patterns extracted from models cannot be assessed purely by focusing on prediction accuracy our results instead suggest that model regularization parameters must be carefully selected so that the model and its visualization enhance our ability to interpret the brain

the 3 d unconditionally stable associated hermite finite difference time domain method this article discussed the implementation of 3 d associated hermite ah finite difference time domain fdtd method and its application in the analysis of lumped parameters modeling based on the paralleling in order scheme the basic formula of the 3 d ah fdtd method incorporated with complex frequency shifted cfs perfectly matched layer pml absorption boundary was derived then by using the kronecker product scheme the coefficient matrix of the final matrix equation was fast generated and by using the permutation technology its bandwidth was compressed updating the coefficient matrix and source term of the matrix equation was implemented when dealing with the lumped parameters to solve the final matrix to obtain the ah expanding coefficients the direct or indirect methods mainly by the iterative method could be used finally several numerical results were presented to validate the accuracy and efficiency of the proposed method

an explicit and unconditionally stable fdtd method for the analysis of general 3 d lossy problems the root cause of the instability of an explicit finite difference time domain fdtd method is quantitatively identified for the analysis of general lossy problems where both dielectrics and conductors can be lossy and inhomogeneous based on the root cause analysis an efficient algorithm is developed to eradicate the root cause of instability and subsequently achieve unconditional stability in an explicit fdtd based simulation of general lossy problems numerical experiments have demonstrated the unconditional stability accuracy and efficiency of the proposed method

person re identification by dual regularized kiss metric learning person re identification aims to match the images of pedestrians across different camera views from different locations this is a challenging intelligent video surveillance problem that remains an active area of research due to the need for performance improvement person re identification involves two main steps feature representation and metric learning although the keep it simple and straightforward kiss metric learning method for discriminative distance metric learning has been shown to be effective for the person re identification the estimation of the inverse of a covariance matrix is unstable and indeed may not exist when the training set is small resulting in poor performance here we present dual regularized kiss dr kiss metric learning by regularizing the two covariance matrices dr kiss improves on kiss by reducing overestimation of large eigenvalues of the two estimated covariance matrices and in doing so guarantees that the covariance matrix is irreversible furthermore we provide theoretical analyses for supporting the motivations specifically we first prove why the regularization is necessary then we prove that the proposed method is robust for generalization we conduct extensive experiments on three challenging person re identification datasets viper grid and cuhk 01 and show that dr kiss achieves new state of the art performance

an improved paralleling in order solving scheme for ah fdtd method using eigenvalue transformation this paper describes a modification of the unconditionally stable finite difference time domain fdtd algorithm based on the associated hermite ah functions the original method is involved with solving a banded nested matrix equation to calculate the ah expanding coefficients using lower upper lu decomposition which is the main cost for cpu time and memory storage here the calculation of expansion coefficients is modified as a paralleling in order solving scheme by using the eigenvalue transformation which greatly enhances the computational efficiency and reduces the memory storage the numerical results demonstrate the performance of this improvement

efficient implementation for the ah fdtd method with iterative procedure and cfs pml an efficient implementation for the associated hermite finite difference time domain ah fdtd method with iterative procedure is proposed which is an extension of previous works the original and the paralleling in order based ah fdtd methods by introducing the alternating direction implicit method the direct calculation of five point matrix equation is replaced with iterative solving of tridiagonal matrices equations for the 2 d case only two tridiagonal matrixes need to be calculated in a full iterative cycle in addition the complex frequency shifted cfs perfectly matched layer pml is extended to the ah fdtd method and implemented together with this efficient solution scheme numerical examples show the superior computational performance of the proposed iterative procedure by comparison with the conventional fdtd method the adi fdtd method and previous ah fdtd methods the effectiveness of cfs pml in ah fdtd is also verified

sparse canonical correlation analysis new formulation and algorithm in this paper we study canonical correlation analysis cca which is a powerful tool in multivariate data analysis for finding the correlation between two sets of multidimensional variables the main contributions of the paper are 1 to reveal the equivalent relationship between a recursive formula and a trace formula for the multiple cca problem 2 to obtain the explicit characterization for all solutions of the multiple cca problem even when the corresponding covariance matrices are singular 3 to develop a new sparse cca algorithm and 4 to establish the equivalent relationship between the uncorrelated linear discriminant analysis and the cca problem we test several simulated and real world datasets in gene classification and cross language document retrieval to demonstrate the effectiveness of the proposed algorithm the performance of the proposed method is competitive with the state of the art sparse cca algorithms

cross validation the jackknife and the bootstrap excess error estimation in forward logistic regression abstract given a prediction rule based on a set of patients what is the probability of incorrectly predicting the outcome of a new patient call this probability the true error an optimistic estimate is the apparent error or the proportion of incorrect predictions on the original set of patients and it is the goal of this article to study estimates of the excess error or the difference between the true and apparent errors i consider three estimates of the excess error cross validation the jackknife and the bootstrap using simulations and real data the three estimates for a specific prediction rule are compared when the prediction rule is allowed to be complicated overfitting becomes a real danger and excess error estimation becomes important the prediction rule chosen here is moderately complicated involving a variable selection procedure based on forward logistic regression

subclass graph embedding and a marginal fisher analysis paradigm subspace learning techniques have been extensively used for dimensionality reduction dr in many pattern classification problem domains recently methods like subclass discriminant analysis sda and clustering based discriminant analysis cda which use subclass information for the discrimination between the data classes have attracted much attention in parallel important work has been accomplished on graph embedding ge which is a general framework unifying several subspace learning techniques in this paper ge has been extended in order to integrate subclass discriminant information resulting to the novel subclass graph embedding sge framework which is the main contribution of our work it is proven that sge encapsulates a diversity of both supervised and unsupervised unimodal methods like locality preserving projections lpp principal component analysis pca and linear discriminant analysis lda the theoretical link of sda and cda methods with sge is also established along these lines it is shown that sge comprises a generalization of the typical ge framework including subclass dr methods moreover it allows for an easy utilization of kernels for confronting non linearly separable data employing sge in this paper a novel dr algorithm which uses subclass discriminant information called subclass marginal fisher analysis smfa has been proposed through a series of experiments on various real world datasets it is shown that smfa outperforms in most of the cases the state of the art demonstrating the efficacy and power of sge as a platform to develop new methods highlightsgraph embedding is extended in order to integrate subclass informationthe novel subclass graph embedding framework is proposed the kernelized version of the new framework is presentedsubclass graph embedding encapsulates various subspace learning methods a novel subclass marginal fisher analysis method is proposed

joint deconvolution and classification with applications to passive acoustic underwater multipath this paper addresses the problem of classifying signals that have been corrupted by noise and unknown linear time invariant lti filtering such as multipath given labeled uncorrupted training signals a maximum a posteriori approach to the deconvolution and classification is considered which produces estimates of the desired signal the unknown channel and the class label for cases in which only a class label is needed the classification accuracy can be improved by not committing to an estimate of the channel or signal a variant of the quadratic discriminant analysis qda classifier is proposed that probabilistically accounts for the unknown lti filtering and which avoids deconvolution the proposed qda classifier can work either directly on the signal or on features whose transformation by lti filtering can be analyzed as an example a classifier for subband power features is derived results on simulated data and real bowhead whale vocalizations show that jointly considering deconvolution with classification can dramatically improve classification performance over traditional methods over a range of signal to noise ratios

estimating the error rate of a prediction rule improvement on cross validation abstract we construct a prediction rule on the basis of some data and then wish to estimate the error rate of this rule in classifying future observations cross validation provides a nearly unbiased estimate using only the original data cross validation turns out to be related closely to the bootstrap estimate of the error rate this article has two purposes to understand better the theoretical basis of the prediction problem and to investigate some related estimators which seem to offer considerably improved estimation in small samples

functional linear discriminant analysis for irregularly sampled curves we introduce a technique for extending the classical method of linear discriminant analysis lda to data sets where the predictor variables are curves or functions this procedure which we call functional linear discriminant analysis flda is particularly useful when only fragments of the curves are observed all the techniques associated with lda can be extended for use with flda in particular flda can be used to produce classifications on new test curves give an estimate of the discriminant function between classes and provide a one or two dimensional pictorial representation of a set of curves we also extend this procedure to provide generalizations of quadratic and regularized discriminant analysis

an unconditionally stable one step arbitrary order leapfrog adi fdtd method and its numerical properties an one step arbitrary order leapfrog alternating direction implicit finite difference time domain adi fdtd method is presented its unconditional stability is analytically proven and numerically verified its numerical properties are shown to be identical to those of the conventional adi fdtd and lod fdtd methods however its computational expenditure is found to be the lowest in other words in comparison with the adi fdtd and lod fdtd methods the one step arbitrary order leap frog adi fdtd method retains identical numerical modeling accuracy but with higher computational efficiency

a quasilinear dirichlet problem with quadratic growth respect to the gradient and l1 data abstract for an open bounded set r n measurable bounded functions a x b x which are strictly positive and p q 0 we prove the existence of a weak solution of the quasilinear b v p div a x u q u x b x u u p 1 u 2 f x x u x 0 x the datum f is assumed to be in l 1 and does not satisfy any sign assumption

efficient integration of sufficient dimension reduction and prediction in discriminant analysis abstractsufficient dimension reduction sdr methods are popular model free tools for preprocessing and data visualization in regression problems where the number of variables is large unfortunate

estimation of a covariance matrix under stein s loss on decrit la technique generale de stein pour ameliorer l estimateur sans biais invariant le meilleur et l estimateur minimax de la matrice de covariance normale

bilinear probabilistic principal component analysis probabilistic principal component analysis ppca is a popular linear latent variable model for performing dimension reduction on 1 d data in a probabilistic manner however when used on 2 d data such as images ppca suffers from the curse of dimensionality due to the subsequently large number of model parameters to overcome this problem we propose in this paper a novel probabilistic model on 2 d data called bilinear ppca bppca this allows the establishment of a closer tie between bppca and its nonprobabilistic counterpart moreover two efficient parameter estimation algorithms for fitting bppca are also developed experiments on a number of 2 d synthetic and real world data sets show that bppca is more accurate than existing probabilistic and nonprobabilistic dimension reduction methods

acoustic evidence for dynamic formant trajectories in australian english vowels the extent to which it is necessary to model the dynamic behavior of vowel formants to enable vowel separation has been the subject of debate in recent years to investigate this issue a study has been made on the vowels of 132 australian english speakers male and female the degree of vowel separation from the formant values at the target was contrasted to that from modeling the formant contour with discrete cosine transform coefficients the findings are that although it is necessary to model the formant contour to separate out the diphthongs the formant values at the target plus vowel duration are sufficient to separate out the monophthongs however further analysis revealed that there are formant contour differences which benefit the within class separation of the tense lax monophthong pairs

robust singular value decompositions a new approach to projection pursuit abstract robust location and covariance estimators are developed via general m estimation for covariance matrix eigenvectors and eigenvalues the solution to this gm estimation problem is obtained by transforming it into a series of robust regression problems based on a new algorithm for the singular value decomposition it is shown here that the singular value decomposition can be represented as an iteration of two steps a least squares regression fit of the data matrix followed by a rotation to the regression hyperplanes an algorithm to obtain the solution to this gm estimation problem is presented along with results of a monte carlo study and examples of its application in addition it is shown how the output of this algorithm can be used to numerically search for multivariate outliers which is especially useful in exploratory data analysis with high dimensional data and large sample sizes where standard graphical techniques are difficult to implement because the algorithm computes robust estima

novel fisher discriminant classifiers at the present several applications need to classify high dimensional points belonging to highly unbalanced classes unfortunately when the training set cardinality is small compared to the data dimensionality small sample size problem the classification performance of several well known classifiers strongly decreases similarly the classification accuracy of several discriminative methods decreases when non linearly separable and unbalanced classes are treated in this paper we firstly survey state of the art methods that employ improved versions of linear discriminant analysis lda to deal with the above mentioned problems secondly we propose a family of classifiers based on the fisher subspace estimation which efficiently deal with the small sample size problem non linearly separable classes and unbalanced classes the promising results obtained by the proposed techniques on benchmark datasets and the comparison with state of the art predictors show the efficacy of the proposed techniques

efficient and robust feature extraction by maximum margin criterion in pattern recognition feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information principal component analysis pca and linear discriminant analysis lda are the two most popular linear dimensionality reduction methods however pca is not very effective for the extraction of the most discriminant features and lda is not stable due to the small sample size problem in this paper we propose some new linear and nonlinear feature extractors based on maximum margin criterion mmc geometrically feature extractors based on mmc maximize the average margin between classes after dimensionality reduction it is shown that mmc can represent class separability better than pca as a connection to lda we may also derive lda from mmc by incorporating some constraints by using some other constraints we establish a new linear feature extractor that does not suffer from the small sample size problem which is known to cause serious stability problems for lda the kernelized nonlinear counterpart of this linear feature extractor is also established in the paper our extensive experiments demonstrate that the new feature extractors are effective stable and efficient

a new unconditionally stable scheme for fdtd method using associated hermite orthogonal functions an unconditionally stable solution using associated hermite ah functions is proposed for the finite difference time domain fdtd method the electromagnetic fields and their time derivatives in time do main maxwell s equations are expanded by these orthonormal basis func tions by applying galerkin temporal testing procedure to these expanded equations the time variable can be eliminated from the calculations a set of implicit equations is derived to calculate the magnetic filed expansion coefficients of all orders of ah functions for the temporal variable and the electrical field coefficients can be obtained respectively with the ap propriate translation and scale parameters we can find a minimum order basis functions subspace to approach a particular electromagnetic field the numerical results have shown that the proposed method can reduce the cpu time to 0 59 of the traditional fdtd method while maintaining good accuracy

likelihood ratio forensic voice comparison using parametric representations of the formant trajectories of diphthongs non contemporaneous speech samples from 27 male speakers of australian english were compared in a forensic likelihood ratio framework parametric curves polynomials and discrete cosine transforms were fitted to the formant trajectories of the diphthongs a e o a and the estimated coefficient values from the parametric curves were used as input to a generative multivariate kernel density formula for calculating likelihood ratios expressing the probability of obtaining the observed difference between two speech samples under the hypothesis that the samples were produced by the same speaker versus under the hypothesis that they were produced by different speakers cross validated likelihood ratio results from systems based on different parametric curves were calibrated and evaluated using the log likelihood ratio cost function cllr the cross validated likelihood ratios from the best performing system for each vowel phoneme were fused using logistic regression the resulting fused system had a very low error rate thus meeting one of the requirements for admissibility in court

kernel quadratic discriminant analysis for small sample size problem it is generally believed that quadratic discriminant analysis qda can better fit the data in practical pattern recognition applications compared to linear discriminant analysis lda method this is due to the fact that qda relaxes the assumption made by lda based methods that the covariance matrix for each class is identical however it still assumes that the class conditional distribution is gaussian which is usually not the case in many real world applications in this paper a novel kernel based qda method is proposed to further relax the gaussian assumption by using the kernel machine technique the proposed method solves the complex pattern recognition problem by combining the qda solution and the kernel machine technique and at the same time tackles the so called small sample size problem through a regularized estimation of the covariance matrix extensive experimental results indicate that the proposed method is a more sophisticated solution outperforming many traditional kernel based learning algorithms

penalized discriminant analysis fisher s linear discriminant analysis lda is a popular data analytic tool for studying the relationship between a set of predictors and a categorical response in this paper we describe a penalized version of lda it is designed for situations in which there are many highly correlated predictors such as those obtained by discretizing a function or the grey scale values of the pixels in a series of images in cases such as these it is natural efficient and sometimes essential to impose a spatial smoothness constraint on the coefficients both for improved prediction performance and interpretability we cast the classification problem into a regression framework via optimal scoring using this our proposal facilitates the use of any penalized regression technique in the classification setting the technique is illustrated with examples in speech recognition and handwritten character recognition

spectral shape features versus formants as acoustic correlates for vowels the first three formants i e the first three spectral prominences of the short time magnitude spectra have been the most commonly used acoustic cues for vowels ever since the work of peterson and barney j acoust soc am 24 175 184 1952 however spectral shape features which encode the global smoothed spectrum provide a more complete spectral description and therefore might be even better acoustic correlates for vowels in this study automatic vowel classification experiments were used to compare formants and spectral shape features for monopthongal vowels spoken in the context of isolated cvc words under a variety of conditions the roles of static and time varying information for vowel discrimination were also compared spectral shape was encoded using the coefficients in a cosine expansion of the nonlinearly scaled magnitude spectrum under almost all conditions investigated in the absence of fundamental frequency f0 information automatic vowel classification based on spectral shape features was superior to that based on formants if f0 was used as an additional feature vowel classification based on spectral shape features was still superior to that based on formants but the differences between the two feature sets were reduced it was also found that the error pattern of perceptual confusions was more closely correlated with errors in automatic classification obtained from spectral shape features than with classification errors from formants therefore it is concluded that spectral shape features are a more complete set of acoustic correlates for vowel identity than are formants in comparing static and time varying features static features were the most important for vowel discrimination but feature trajectories were valuable secondary sources of information

enhanced fisher discriminant criterion for image recognition many previous studies have shown that image recognition can be significantly improved by fisher linear discriminant analysis flda technique however flda ignores the variation of data points from the same class which characterizes the most important modes of variability of patterns and helps to improve the generalization capability of flda thus the performance of flda on testing data is not good enough in this paper we propose an enhanced fisher discriminant criterion efdc efdc explicitly considers the intra class variation and incorporates the intra class variation into the fisher discriminant criterion to build a robust and efficient dimensionality reduction function efdc obtains a subspace which best detects the discriminant structure and simultaneously preserves the modes of variability of patterns which will result in stable intraclass representation experimental results on four image database show a clear improvement over the results of flda based methods