[1]LIU Xiangnan,DING Shifei,WANG Lijuan.A multi-view clustering algorithm based on deep matrix factorization with graph regularization[J].CAAI Transactions on Intelligent Systems,2022,17(1):158-169.[doi:10.11992/tis.202104046]
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A multi-view clustering algorithm based on deep matrix factorization with graph regularization

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 1 Page number: 158-169 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2022-01-05
Title:
A multi-view clustering algorithm based on deep matrix factorization with graph regularization
Author(s):
LIU Xiangnan1;  DING Shifei1;  WANG Lijuan1; 2
1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;
2. School of Information and Electrical Engineering, Xuzhou College of Industrial Technology, Xuzhou 221400, China
Keywords:
multi-view clustering; deep matrix factorization; geometric structure; graph regularization; matrix factorization; multi-view representation learning; hierarchical structure information; deep learning
CLC:
TP181
DOI:
10.11992/tis.202104046
Abstract:
In view of the extensive multi-view data composed of multiple representations or views in real world, the deep matrix factorization (DMF) model has attracted much attention because of its ability to explore the hierarchical information of data. However, it ignores geometric structure of data. In order to solve the above problem, this paper proposes a multi-view clustering algorithm based on deep matrix factorization with graph regularization, which can protect geometric structure information of data by acquiring the local and global structure information of each view and adding two graph regularization limits in the layer-by-layer decomposition. It combines the weight of views with feature representation matrix to acquire consensus representation matrix to maximize complementarity of data and ensure consistency and difference among data. In addition, this paper uses the iterative updating variables method to minimize the objective function, continuously optimize model and conduct convergence analysis. This algorithm and other multiple algorithms are run on three face benchmark datasets and two image data sets. Through the comparison of multiple indicators, it can be seen that the algorithm proposed in this paper has good performance.
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