In many applications, data come with a natural ordering. This ordering can often induce local dependence among nearby variables. However, in complex data, the width of this dependence may vary, making simple assumptions such as a constant neighborhood size unrealistic. We propose a framework for learning this local dependence based on estimating the inverse of the Cholesky factor of the covaria...

This paper describes an experiment investigating how well same-speaker speech samples can be discriminated from different-speaker speech samples using acoustic parameters from Australian English diphthongs. A two-level kernel density multivariate likelihood ratio is used as a discriminant function on five of the diphthongs of the 171 speakers of the Bernard data base: /a    /. Comparin...

This paper is concerned with the problem of recognition of dynamic hand gestures. We have considered gestures which are sequences of distinct hand poses. In these gestures hand poses can undergo motion and discrete changes. However, continuous deformations of the hand shapes are not permitted. We have developed a recognition engine which can reliably recognize these gestures despite individual ...

The study aligns marketing intelligence approaches with website performance assessment for evaluating the effectiveness of a company’s relational marketing strategies (RMS) in the performance of its corporate website. We develop a web mining based methodology that combines a classification procedure with discriminant analysis for analyzing clickstream data. Furthermore, we integrate managerial ...

A general method for exploring multivariate data by comparing different estimates of multivariate scatter is presented. The method is based upon the eigenvalue-eigenvector decomposition of one scatter matrix relative to another. In particular, it is shown that the eigenvectors can be used to generate an affine invariant coordinate system for the multivariate data. Consequently, we view this met...

This paper presents a new offline face recognition system. The proposed system is built on one dimensional left-toright Hidden Markov Models (1D-HMMs). Facial image features are extracted using Gabor wavelets. The dimensionality of these features is reduced using the Fisher’s Discriminant Analysis method to keep only the most relevant information. Unlike existing techniques using 1D-HMMs, in cl...

In this work, a novel approach is proposed to recognize some facial expressions from time-sequential depth videos. Local Directional Pattern (LDP) features are extracted from the time-sequential depth faces that are followed by Linear Discriminant Analysis (LDA) to make the features more robust. Finally, the robust local features are applied with Hidden Markov Models (HMMs) for facial expressio...

We discuss approaches to incrementally construct an ensemble. The first constructs an ensemble of classifiers choosing a subset from a larger set, and the second constructs an ensemble of discriminants, where a classifier is used for some classes only. We investigate criteria including accuracy, significant improvement, diversity, correlation, and the role of search direction. For discriminant ...

Human action recognition from video sequences is a challenging problem due to the large changes of human appearance in the cases of partial occlusions, non-rigid deformations, and high irregularities. It is difficult to collect a large set of training samples to learn the discriminative model with covering all possible variations of an action. In this paper, we propose an online recognition met...

In recent years, a considerable amount of work has been devoted to generalizing linear discriminant analysis to overcome its incompetence for high-dimensional classification (Witten and Tibshirani, 2011, Cai and Liu, 2011, Mai et al., 2012 and Fan et al., 2012). In this paper, we develop high-dimensional sparse semiparametric discriminant analysis (SSDA) that generalizes the normal-theory discr...