L1 norm based KPCA for novelty detection

Authors:
Yingchao Xiao;Huangang Wang;Wenli Xu;Junwu Zhou
Affiliations:
Institute of Control Theory and Technology, Department of Automation, Tsinghua University, Beijing 100084, China;Institute of Control Theory and Technology, Department of Automation, Tsinghua University, Beijing 100084, China;Institute of Control Theory and Technology, Department of Automation, Tsinghua University, Beijing 100084, China;Beijing General Research Institute of Mining & Metallurgy, Beijing 100070, China
Venue:
Pattern Recognition
Year:
2013

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Abstract

Novelty detection is a one class classification problem, and it builds up the model with only normal samples, based on which the novelty is detected. Though conventional KPCA is an effective method of building one class classification models, it is prone to being affected by the presence of outliers due to its inherent properties of L2 norm. In this paper, we propose a new optimization problem, L1 norm based KPCA, which is robust to outliers. Correspondingly, we present the algorithm and the measure of novelty. The proposed method is applied to novelty detection and performs well on the simulation data sets.