Concept Factorization-Based Collaborative Multi-view Clustering Algorithm in Visible and Latent Spaces
HU Suting1, SHEN Zongxin1, HUANG Qianqian2,3, HUANG Yanyong1
1. School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130; 2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756; 3. School of Computer Science and Technology, Southwest Minzu University, Chengdu 610041
Abstract Multi-view clustering effectively improves the clustering performance by integrating the features derived from different views. The existing multi-view clustering methods more focus on different low-dimensional representations of data and their geometrical structures in latent space, while ignoring the structural relations of data in different spaces and the clustering of different spaces. To address this issue, a concept factorization-based collaborative multi-view clustering algorithm in visible and latent spaces is proposed in this paper. Firstly, common low-dimensional feature representation of different views in latent space is extracted through concept factorization. Besides, the local structure of the original data is preserved by means of graph Laplacian regularization. Then, the data clustering in visible and latent spaces are integrated into a unified framework for collaborative learning and optimizing to obtain the final clustering results. Experimental results on eight real datasets show the superiority of the proposed method.
Fund:Youth Fund Project of Humanities and Social Science Foundation of Ministry of Education(No.21YJCZH045)
Corresponding Authors:
HUANG Yanyong, Ph.D., professor. His research interests include machine learning and data mining.
About author:: HU Suting, master student. Her research interests include machine learning and data mining.SHEN Zongxin, Ph.D. candidate. His research interests include data mining and pa?ttern recognition.HUANG Qianqian, Ph.D., lecturer. Her research interests include data mining, granular computing and rough sets.
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2023, Vol. 36 
