An Equalized Heteroscedastic Linear Discriminant Analysis Algorithm

被引：2

作者：

Zhang, Wei-Qiang ^{[1
]}

Liu, Jia ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Elect Engn, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China

来源：

IEEE SIGNAL PROCESSING LETTERS | 2008年 / 15卷

基金：

中国国家自然科学基金;

关键词：

Equalization; feature extraction; heteroscedastic linear discriminant analysis (HLDA);

D O I：

10.1109/LSP.2008.2001561

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：585 / 588

页数：4

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