基于概念分解的显隐空间协同多视图聚类算法

doi:10.16451/j.cnki.issn1003-6059.202302005

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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.

Key words： Multi-view Clustering Co-training Concept Factorization Laplacian Regularization

Received: 23 August 2022

ZTFLH:

TP391

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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	HU Suting
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Cite this article:

HU Suting,SHEN Zongxin,HUANG Qianqian等. Concept Factorization-Based Collaborative Multi-view Clustering Algorithm in Visible and Latent Spaces[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(2): 160-173.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202302005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I2/160

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