Article ID Journal Published Year Pages File Type
530941 Pattern Recognition 2013 13 Pages PDF
Abstract

•Generalized mean is used for BDA in place of arithmetic mean.•The proposed method is more robust to outliers than the other alternative methods.•We also propose a novel method to efficiently maximize the generalized mean.

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.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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