Channel compensation is an integral part for any state-of-theart speaker recognition system. Typically, Linear Discriminant Analysis (LDA) is used to suppress directions containing channel information. LDA assumes a unimodal Gaussian distribution of the speaker samples to maximize the ratio of the between-speaker variance to within-speaker variance. However, when speaker samples have multi-moda...

Although widely used, there are still open questions concerning which properties of Linear Discriminant Analysis (LDA) do account for its success in many speech recognition systems. In order to gain more insight into the nature of the transformation we compare LDA with mel-cepstral feature vectors with respect to the following criteria: decorrelation and ordering property, invariance under line...

Supervised Fisher Linear Discriminant Analysis (LDA) is a classical dimensionality reduction approach. LDA assumes each class has a Gaussian density and may suffer from the singularity problem when handling high-dimensional data. We in this work consider more general class densities and show that optimizing LDA criterion cannot always achieve maximum class discrimination with the geometrical ba...

Linear discriminant analysis (LDA) is a classical statistical approach for dimensionality reduction and classification. In many cases, the projection direction of the classical and extended LDA methods is not considered optimal for special applications. Herein we combine the Partial Least Squares (PLS) method with LDA algorithm, and then propose two improved methods, named LDA-PLS and ex-LDA-PL...

A commercially available Cyranose-320. conducting polymer-based electronic nose system was used to analyze the volatile organic compounds emanating from fresh beef strip loins (M. Longisimmus lumborum) stored at 4°C and 10°C. Two statistical techniques, i.e., linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), were used to develop classification models from the collect...

This paper investigates the use of Fisher-Rao linear discriminant analysis (LDA) as a means of visual feature extraction for hidden Markov model based automatic speechreading. For every video frame, a three-dimensional region of interest containing the speaker's mouth over a sequence of adjacent frames is lexicographically arranged into a data vector. Such vectors are then projected onto the sp...

Using in situ hyperspectral measurements collected in the Sierra Nevada Mountains in California, we discriminate six species of conifer trees using a recent, nonparametric statistics technique known as penalized discriminant analysis (PDA). A classification accuracy of 76% is obtained. Our emphasis is on providing an intuitive, geometric description of PDA that makes the advantages of penalizat...

[1] The decision boundaries of most tectonic discrimination diagrams are drawn by eye. Discriminant analysis is a statistically more rigorous way to determine the tectonic affinity of oceanic basalts based on their bulk-rock chemistry. This method was applied to a database of 756 oceanic basalts of known tectonic affinity (ocean island, mid-ocean ridge, or island arc). For each of these trainin...

Given the nonlinear manifold structure of facial images, a new kernel-based supervised manifold learning algorithm based on locally linear embedding (LLE), called discriminant kernel locally linear embedding (DKLLE), is proposed for facial expression recognition. The proposed DKLLE aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the...