Robust principal component analysis with non-greedy l1-norm maximization

Authors:
Feiping Nie;Heng Huang;Chris Ding;Dijun Luo;Hua Wang
Affiliations:
Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas;Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas;Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas;Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas;Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas
Venue:
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Year:
2011

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Abstract

Principal Component Analysis (PCA) is one of the most important methods to handle high-dimensional data. However, the high computational complexity makes it hard to apply to the large scale data with high dimensionality, and the used l2-norm makes it sensitive to outliers. A recent work proposed principal component analysis based on l1-normmaximization, which is efficient and robust to outliers. In that work, a greedy strategy was applied due to the difficulty of directly solving the l1-norm maximization problem, which is easy to get stuck in local solution. In this paper, we first propose an efficient optimization algorithmto solve a general l1-norm maximization problem, and then propose a robust principal component analysis with non-greedy l1-norm maximization. Experimental results on real world datasets show that the nongreedy method always obtains much better solution than that of the greedy method.