[1]WANG Lijuan,DING Shifei,XIA Jing.Multiview low-rank sparse subspace clustering based on diversity[J].CAAI Transactions on Intelligent Systems,2023,18(2):399-408.[doi:10.11992/tis.202110026]
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Multiview low-rank sparse subspace clustering based on diversity

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 2 Page number: 399-408 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2023-05-05
Title:
Multiview low-rank sparse subspace clustering based on diversity
Author(s):
WANG Lijuan1; 2;  DING Shifei1;  XIA Jing1
1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;
2. School of Information Engineering, Xuzhou College of Industrial Technology, Xuzhou 221114, China
Keywords:
multiview clustering; subspace representation; diversity representation; low-rank sparse constraint; spectral clustering; machine learning; feature learning; data mining
CLC:
TP391
DOI:
10.11992/tis.202110026
Abstract:
This paper focuses on boosting multiview clustering by exploring the diversity of information among multiview features. A multiview clustering framework, called multiview low-rank sparse subspace clustering based on diversity, is proposed for this task. In the proposed method, the concept of diversity is successfully introduced into the framework of a multiview low-rank sparse subspace clustering algorithm to ensure that the representation matrix of different subspace views has certain differences and that the obtained information is diversified. In addition, the spectral clustering algorithm is added to the framework to achieve a joint optimization solution, which can markedly improve the clustering performance, to obtain a unified target clustering assignment. The effectiveness of the algorithm is verified by fully observing three image datasets, and the clustering performance of the proposed algorithm is better than that of the existing single-view and multiview algorithms.
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