ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2022, Vol. 59 ›› Issue (4): 907-921.doi: 10.7544/issn1000-1239.20200897

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Multi-View Clustering Based on Two-Level Weights

Du Guowang, Zhou Lihua, Wang Lizhen, Du Jingwei   

  1. (School of Information Science & Engineering, Yunnan University, Kunming 650500)
  • Online:2022-04-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (62062066, 61762090, 61966036), Yunnan Fundamental Research Projects in 2022, the Project of the University Key Laboratory of Internet of Things Technology and Application of Yunnan Province, the National Social Science Foundation of China (18XZZ005), the Program for Innovation Research Team (in Science and Technology) in University of Yunnan Province (IRTSTYN), and the Scientific Research Fund Project of Yunnan Provincial Department of Education (2021Y026).

Abstract: In the process of clustering, the high-dimensionality and sparsity of multi-view data make the different features of samples described in a view have different effects on the clustering results, and each sample has different contributions to the clustering in different views. Hierarchically distinguishing the weights of different features in one view and the weights of the same sample in different views is an important factor to improve the quality of multi-view clustering. In this paper, we propose a multi-view clustering algorithm based on two-level weights, i.e. feature-level and sample-level weights. The proposed algorithm is named MVC2W, which learns the weights of different features in each view and the weights of each sample in different views by introducing a feature-level and a sample-level attention mechanism. The introduction of the two-level attention mechanism allows the algorithm to pay more attention to important features and important samples during the training process, and to integrate information from different views in a more rational way, thereby alleviating effectively the effects induced by high-dimensionality and sparsity on clustering quality. In addition, MVC2W integrates the process of representation learning and clustering into a unified framework for collaborative training and mutual promotion, so as to further improve the clustering performance. The experimental results on 5 datasets with different degrees of sparseness show that MVC2W algorithm outperforms 11 baseline algorithms, especially in the datasets with high degree of sparseness, and the improvement of clustering performance obtained by MVC2W is more significant.

Key words: multi-view clustering, feature-level weights, sample-level weights, attention mechanism, sparseness

CLC Number: