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Self-weighted graph learning for multi-view clustering
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DOI：10.1016/j.neucom.2022.06.009.png)
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The graph-based multi-view clustering has received extensive attention in recent years due to its competitiveness in characterizing the relationship between data and its well defined mathematic. However, the existing graph-based clustering methods only take into account the data similarity of intra-view, while neglecting the similarity of inter-view. Thus, they cannot well exploit the complementary information and spatial structure embedded in graphs of different views. Second, all of them require that the geometric relationship between the data is exactly the same in different views, which makes no sense in real applications. In this paper, we relax this strict constraint and propose an efficient graph learning model for multi-view clustering. Our proposed method considers the similarity of inter-view by minimizing the tensor Schatten p-norm on the third-order tensor whose lateral slices are composed of graphs of different views. Thus, our method exploits the complementary information and spatial structure. Experiments indicate that our method is superior to some state-of-the-art methods. (c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Multi-view clustering
Graph learning
Rank constraint
Tensor Schatten p -norm
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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