基于正交约束的分块不完整多视角聚类

doi:10.16451/j.cnki.issn1003-6059.202001005

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Abstract Existing incomplete multi-view clustering algorithms based on nonnegative matrix factorization(NMF) cannot extract local features accuratly. To solve this problem, an algorithm of chunk-by-chunk incomplete multi-view clustering based on orthogonal constraints (CIMVCO) is proposed. A potential feature matrix of all views is obtained by nonnegative matrix factorization, and orthogonal constraints are added to obtain better local features. For missing samples of each view, smaller weights are given to reduce the impact of missing data. To solve the problem of large scale data clustering, data are processed block-by-block to reduce the memory demand and processing time. Experimental results on Reuters and Digit datasets demonstrate the effectiveness of CIMVCO.

Key words： Chunk-by-Chunk Multi-view Clustering Nonnegative Matrix Factorization(NMF) Orthogonal Constraints

Received: 14 October 2019

ZTFLH:

TP 181

Fund:Supported by National Natural Science Foundation of China(No.61603243), China Postdoctoral Science Foundation(No.2017M
611503)

Corresponding Authors: YIN Jun, Ph.D., associate professor. His research interests include pattern recognition and machine lear-ning.

About author:: JIANG Jianwei, master student. Her research interests include pattern recognition and machine learning.

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JIANG Jianwei,YIN Jun. Chunk-by-Chunk Incomplete Multi-view Clustering Based on Orthogonal Constraints[J]. , 2020, 33(1): 41-49.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202001005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I1/41

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