ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2022, Vol. 59 ›› Issue (4): 922-935.doi: 10.7544/issn1000-1239.20200875

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Multi-View Clustering with Spectral Structure Fusion

Liu Jinhua1, Wang Yang2, Qian Yuhua3   

  1. 1(Fenyang College of Shanxi Medical University, Fenyang, Shanxi 032200);2(North Automatic Control Technology Institute, Taiyuan 030006);3(Institute of Big Data Science and Industry, Shanxi University, Taiyuan 030006)
  • Online:2022-04-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61672332), the Key Research and Development Program of Shanxi Province (201903D421003), and the Program for the San Jin Young Scholars of Shanxi Province (2016769).

Abstract: Multi-view clustering is an important and challenged task due to the difficulty of integrating information from diverse views. After years of research it still faces two challenged questions to date. First, how to integrate heterogeneous information from different views effectively and reduce information loss. Second, how to perform graph learning and spectral clustering simultaneously, avoiding suboptimal clustering results caused by two-step strategy. Integrating heterogeneous information in the data space may cause significant information loss because of unavoidable noise hidden in the data itself or inconsistency among views. Moreover, we consider the case that different views admit the same cluster structure. To fill these gaps, a novel multi-view clustering model with spectral structure fusion is proposed, which fuses the information in the stage of spectral embedding. On the one hand, it avoids the influence of noise and difference of data from diverse views; on the other hand, the fusion position and method are more natural, which reduces the loss of information in the fusion stage. Besides, the model utilizes subspace self-representation for graph learning and integrates graph learning and spectral clustering into a unified framework effectively by joint optimization learning. Experiments on five widely used data sets confirm the superiority and validity of the proposed method.

Key words: multi-view clustering, spectral embedding structure, information fusion, subspace self-representation, joint optimization.

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