On kernel discriminant analyses applied to phoneme classification

Authors:
András Kocsor
Affiliations:
Hungarian Academy of Sciences and University of Szeged, Szeged, Hungary
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

In this paper we recall two kernel methods for discriminant analysis. The first one is the kernel counterpart of the ubiquitous Linear Discriminant Analysis (Kernel-LDA), while the second one is a method we named Kernel Springy Discriminant Analysis (Kernel-SDA). It seeks to separate classes just as Kernel-LDA does, but by means of defining attractive and repulsive forces. First we give technical details about these methods and then we employ them on phoneme classification tasks. We demonstrate that the application of kernel functions significantly improves the recognition accuracy.