Adaptive weighted nonnegative low-rank representation
Wen, Jie; Zhang, Bob; Xu, Yong; Yang, Jian; Han, Na
AbstractConventional graph based clustering methods treat all features equally even if they are redundant features or noise in the stage of graph learning, which is obviously unreasonable. In this paper, we propose a novel graph learning method named adaptive weighted nonnegative low-rank representation (AWNLRR) for data clustering. Based on the observation that noise and outliers usually cannot be represented well and suffer from larger reconstruction errors than the important features (clean features) in low-rank or sparse representation, we impose an adaptive weighted matrix on the data reconstruction errors to reinforce the role of the important features in the joint representation and thus a robust graph can be obtained. In addition, a locality constraint, i.e., distance regularization term, is introduced to capture the local structure of data and enable the obtained graph to be sparser. These appealing properties allow AWNLRR to well capture the intrinsic structure of data, and thus AWNLRR has potential to achieve a better clustering performance than other methods. Experimental results on synthetic and real databases show that the proposed method obtains the best clustering performance than some state-of-the-art methods. (C) 2018 Elsevier Ltd. All rights reserved.
KeywordLow-rank representation Adaptive weighted matrix Data clustering Locality constraint
URLView the original
Indexed BySCI
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000436350700025
The Source to ArticleWOS
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Cited Times [WOS]:26   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
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GB/T 7714
Wen, Jie,Zhang, Bob,Xu, Yong,et al. Adaptive weighted nonnegative low-rank representation[J]. PATTERN RECOGNITION,2018,81:326-340.
APA Wen, Jie,Zhang, Bob,Xu, Yong,Yang, Jian,&Han, Na.(2018).Adaptive weighted nonnegative low-rank representation.PATTERN RECOGNITION,81,326-340.
MLA Wen, Jie,et al."Adaptive weighted nonnegative low-rank representation".PATTERN RECOGNITION 81(2018):326-340.
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