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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)
作者部门信息服务与智能控制技术研究室
关键词Evolutionary Algorithm Multiobjective Optimization Mutation Robust Principal Component Analysis
发表期刊Journal of Computational and Theoretical Nanoscience
ISSN1546-1955
2016
卷号13期号:6页码:3722-3733
收录类别EI
EI收录号20164002870074
产权排序3
摘要Robust 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.
语种英语
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/19927
专题信息服务与智能控制技术研究室
通讯作者Chen HN(陈瀚宁)
作者单位1.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
推荐引用方式
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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