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Generalized mean for feature extraction in one-class classification problems
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DOI：10.1016/j.patcog.2013.06.018.png)
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摘要
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Biased discriminant analysis (BDA), which extracts discriminative features for one-class classification problems, is sensitive to outliers in negative samples. This study focuses on the drawback of BDA attributed to the objective function based on the arithmetic mean in one-class classification problems, and proposes an objective function based on a generalized mean. A novel method is also presented to effectively maximize the objective function. The experimental results show that the proposed method provides better discriminative features than the BDA and its variants. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
Generalized mean
Biased discriminant analysis
Feature extraction
Dimensionality reduction
One-class classification
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7.6
论文数:
1.3W
被引数:
4.5W
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