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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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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)
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