Robust Nonconvex Nonnegative Low-rank Representation
Zhao,Yin Ping1; Lu,Xiliang2; Chen,Long1; Tian,Jinyu1; Chen,C. L.Philip1
Source Publication2019 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2019
AbstractLow-rank representation (LRR) has drawn increasing attention in many areas due to its pleasing efficiency in finding subspaces in high-dimensional data. However, the performance of LRR is effected by two problems. First, LRR may generate negative coding coefficients which lack physical meaning. Second, LRR usually obtains a suboptimal solution since the nuclear norm ||. ||∗ is a loose approximation of the rank function rank(.). To solve the limitations simultaneously, we propose a novel model named Robust Nonconvex Nonnegative Low-rank Representation, termed as RNNLRR. Besides, to rule out the trivial solution, diagonal elements of the coding coefficients are constrained to zero. Based on the alternating direction method of multipliers, an efficient optimization algorithm is derived to solve our model. Experiments on data clustering and noise removal demonstrate the superiority of the proposed RNNLRR.
KeywordADMM low-rank representation nonconvex Nonnegative regularization
URLView the original
Scopus ID2-s2.0-85084192833
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Document TypeConference paper
CollectionUniversity of Macau
Corresponding AuthorChen,Long
Affiliation1.Faculty of Science and Technology,University of Macau,Taipa,Macao
2.Faculty of Mathematics and Statistics,Wuhan University,Wuhan,China
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Zhao,Yin Ping,Lu,Xiliang,Chen,Long,et al. Robust Nonconvex Nonnegative Low-rank Representation[C],2019:226-231.
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