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An Equalized Heteroscedastic Linear Discriminant Analysis Algorithm
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DOI：10.1109/LSP.2008.2001561.png)
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摘要
En 中文
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.
Keyword:
Equalization
feature extraction
heteroscedastic linear discriminant analysis (HLDA)
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对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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