Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (6): 1092-1102.DOI: 10.3778/j.issn.1673-9418.2005015

• Artificial Intelligence • Previous Articles     Next Articles

Multi-view Fuzzy Clustering Combining Visual and Hidden Information with Feature Weighting

LIANG Ling, DENG Zhaohong, WANG Shitong   

  1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Online:2021-06-01 Published:2021-06-03



  1. 江南大学 人工智能与计算机学院,江苏 无锡 214122


Multi-view clustering is a type of multi-view learning method applied to unsupervised learning, which aims to use the feature set of different views to enhance the effect of clustering. Although multi-view clustering has been effectively applied in many fields, it still faces many challenges. For example, traditional algorithms only use visual information for clustering, ignoring the importance of hidden information. This paper proposes a multi-view fuzzy clustering algorithm (MVSH) that considers both visual and hidden information and feature weighting. This algorithm implements collaborative learning from various views under the framework of fuzzy clustering. On the one hand, personalized information is obtained by clustering features weighted under each view. On the other hand, the coefficient matrix shared by multi-view data sets is extracted by feature learning to obtain common (hidden) information. A multi-view learning with visual and hidden collaboration is realized. Using visual information and hidden information can better balance the common information and personalized information in the process of multi-view clustering collaborative learning. Experimental studies on multi-view data sets also effectively verify the above advantages of MVSH. Comparison with the performance of multiple related algorithms shows that the proposed method can achieve better or at least comparable performance. Experiments on multiple multi-view data sets prove that the strategy of merging visual and hidden information proposed in this paper has better effect than using visual and hidden information alone.

Key words: hidden information, feature weighting, multi-view learning, collaborative learning



关键词: 隐信息, 特征加权, 多视角学习, 协同学习