S-Space Graduate School of Convergence Science and Technology (융합과학기술대학원) Dept. of Transdisciplinary Studies(융합과학부) Journal Papers (저널논문_융합과학부)
Generalized mean for feature extraction in one-class classification problems
- Oh, Jiyong; Kwak, Nojun; Lee, Minsik; Choi, Chong-Ho
- Issue Date
- Pattern Recognition, Vol.46 No.12, pp. 3328-3340
- 공학; Generalized mean; Biased discriminant analysis; Feature extraction; Dimensionality reduction; One-class classiﬁcation
- 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.
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