关键词: 多视角聚类, 稀疏表示, 隐视角信息, 协同学习

Abstract: Multi-view clustering aims to cluster the data described by different feature sets. Most of the traditional algorithms directly cluster the original feature sets, and ignore the influence of some hidden information on the clustering performance. Some multi-view clustering methods have tried to find hidden information embedded in multi-view data and to cluster based on hidden information, but such algorithms will lose the original feature information to varying degrees. To solve these problems, a multi-view clustering algorithm that is integrated with sparse hidden view information learning is proposed. Firstly, in order to mine the potential sparse hidden view information, a sparse hidden view information learning model is proposed, and the sparse hidden view information is obtained by solving the model. Then, the collaborative learning between the original feature sets and the sparse hidden view information is implemented in the clustering process. Experimental results on real datasets show that the proposed algorithm outperforms the existing clustering algorithms.

Key words: multi-view clustering, sparse representation, hidden view information, collaborative learning