4.7 Article

Incremental multi-view spectral clustering with sparse and connected graph learning

Journal

NEURAL NETWORKS
Volume 144, Issue -, Pages 260-270

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2021.08.031

Keywords

Multi-view clustering; Incremental clustering; Sparse graph learning; Connected graph learning; Spectral embedding

Funding

  1. National Natural Science Foundation of China [61772198, 62072151, U20A20228]
  2. Zhejiang Basic Public Welfare Research Project, China [LGN18F020002]
  3. Natural Science Foundation of Zhejiang Province, China [LR20F020002]
  4. Anhui Provincial Natural Science Fund for Distinguished Young Scholars, China [2008085J30]
  5. Fundamental Research Funds for Central Universities of China [JZ2019HGPA0102]

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This paper proposes an efficient incremental multi-view spectral clustering method SCGL, which only stores one consensus similarity matrix to represent the structural information of all historical views, and integrates sparse graph learning and connected graph learning. Experimental results demonstrate that the method outperforms traditional methods in clustering accuracy.
In recent years, a lot of excellent multi-view clustering methods have been proposed. Because most of them need to fuse all views at one time, they are infeasible as the number of views increases over time. If the present multi-view clustering methods are employed directly to re-fuse all views at each time, it is too expensive to store all historical views. In this paper, we proposed an efficient incremental multi-view spectral clustering method with sparse and connected graph learning (SCGL). In our method, only one consensus similarity matrix is stored to represent the structural information of all historical views. Once the newly collected view is available, the consensus similarity matrix is reconstructed by learning from its previous version and the current new view. To further improve the incremental multi-view clustering performance, the sparse graph learning and the connected graph learning are integrated into our model, which can not only reduce the noises, but also preserve the correct connections within clusters. Experiments on several multi-view datasets demonstrate that our method is superior to traditional methods in clustering accuracy, and is more suitable to deal with the multi-view clustering with the number of views increasing over time. (C) 2021 Elsevier Ltd. All rights reserved.

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