Generalized mean for robust principal component analysis

PATTERN RECOGNITION

卷:54

Article

Oh, Jiyong¹  Kwak, Nojun¹ 

[1] Seoul Natl Univ, Grad Sch Convergence Sci & Technol, AICT, 1 Gwanak Ro, Seoul 08826, South Korea

关键词: Generalized mean;    Principal component analysis;    Robust PCA;    Dimensionality reduction;   

DOI  :  10.1016/j.patcog.2016.01.002

来源: Elsevier

【 摘 要 】

In this paper, we propose a robust principal component analysis (PCA) to overcome the problem that PCA is prone to outliers included in the training set. Different from the other alternatives which commonly replace L-2-norm by other distance measures, the proposed method alleviates the negative effect of outliers using the characteristic of the generalized mean keeping the use of the Euclidean distance. The optimization problem based on the generalized mean is solved by a novel method. We also present a generalized sample mean, which is a generalization of the sample mean, to estimate a robust mean in the presence of outliers. The proposed method shows better or equivalent performance than the conventional PCAs in various problems such as face reconstruction, clustering, and object categorization. (C) 2016 The Authors. Published by Elsevier Ltd.

【 授权许可】

Free