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A NSGA-II with alternating direction method of multipliers mutation for solving multiobjective robust principal component analysis problem
Yuan, Weitao; Liang XD(梁晓丹); Chen HN(陈瀚宁); Lin N(蔺娜); Zou T(邹涛)
Department信息服务与智能控制技术研究室
Source PublicationJournal of Computational and Theoretical Nanoscience
ISSN1546-1955
2016
Volume13Issue:6Pages:3722-3733
Indexed ByEI
EI Accession number20164002870074
Contribution Rank3
KeywordEvolutionary Algorithm Multiobjective Optimization Mutation Robust Principal Component Analysis
AbstractRobust Principal Component Analysis (RPCA), which is a popular parsimony model, is becoming increasingly important for researchers to do data analysis and prediction. The RPCA formulation is made of two components: sparse penalty and low rank penalty. These two competing terms are balanced with one parameter, which is essential for the effectiveness of RPCA. However, in real-world applications, the lack of data adaptive methods for choosing the right parameter hinders the popularization of RPCA. In this work, RPCA is generalized to a multiobjective optimization problem without any balancing parameter. The new model is named as Multiobjective Robust Principal Component Analysis (MRPCA). We aim to solve MRPCA via Evolutionary Algorithm. To the best knowledge of authors, this is the first attempt to use evolutionary algorithm to solve RPCA problem, which is a high dimensional convex optimization problem. Specifically, one of the popular evolutionary algorithm, NSGA-II, is tested on MRPCA problem. The curse of dimensionality is observed when the dimension of MRPCA problem increases. To handle this dimensionality problem, we introduce a novel mutation, termed as Alternating Direction Method of Multipliers mutation (ADMM mutation), that works well in high dimensional decision space. Numerical experiments show that this modified NSGA-II, which converges much faster than the standard one, can deal with the curse of dimensionality well. Furthermore, numerical image reconstruction test confirms that the reconstruction performance of our modified NSGA-II is better than the traditional proximal algorithm, which is usually used to solve RPCA problem.
Language英语
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/19927
Collection信息服务与智能控制技术研究室
Corresponding AuthorChen HN(陈瀚宁)
Affiliation1.School of Computer Science and Software Engineering, Tianjin Polytechnic University, Tanjin, 300387, China
2.Beijing Shenzhou Aerospace Software Technology Company Limited, Beijing, 110000, China
3.Laboratory of Information Service and Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
GB/T 7714
Yuan, Weitao,Liang XD,Chen HN,et al. A NSGA-II with alternating direction method of multipliers mutation for solving multiobjective robust principal component analysis problem[J]. Journal of Computational and Theoretical Nanoscience,2016,13(6):3722-3733.
APA Yuan, Weitao,Liang XD,Chen HN,Lin N,&Zou T.(2016).A NSGA-II with alternating direction method of multipliers mutation for solving multiobjective robust principal component analysis problem.Journal of Computational and Theoretical Nanoscience,13(6),3722-3733.
MLA Yuan, Weitao,et al."A NSGA-II with alternating direction method of multipliers mutation for solving multiobjective robust principal component analysis problem".Journal of Computational and Theoretical Nanoscience 13.6(2016):3722-3733.
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