An Equalized Heteroscedastic Linear Discriminant Analysis Algorithm

Wei-Qiang Zhang

doi:10.1109/LSP.2008.2001561

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An Equalized Heteroscedastic Linear Discriminant Analysis Algorithm

2008-01-01

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张伟强 (Wei-Qiang Zhang) *

Jia Liu

DOI：10.1109/LSP.2008.2001561

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Abstract

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Heteroscedastic linear discriminant analysis (HLDA) is a widely used feature extraction algorithm. This method, however, suffers from unbalanced training data in some cases. In this letter, we equalize the objective function and statistics of HLDA and present an equalized HLDA algorithm, which balances the training data according to the class prior probability. Simulations as well as experimental results for the task of language identification are used to demonstrate the effectiveness of the proposed method.

Keywords:

Equalization

feature extraction

heteroscedastic linear discriminant analysis (HLDA)