Co-consensus semi-supervised multi-view learning with orthogonal non-negative matrix factorization

作     者：Liang, Naiyao Yang, Zuyuan Li, Zhenni Xie, Shengli 

作者机构：Guangdong Univ Technol Guangdong Key Lab IoT Informat Technol Sch Automat Guangzhou 510006 Peoples R China Minist Educ Key Lab iDetect & Mfg IoT Guangzhou 510006 Peoples R China Guangdong HongKong Macao Joint Lab Smart Discrete Guangzhou 510006 Peoples R China 

出 版 物：《INFORMATION PROCESSING & MANAGEMENT》 (信息处理与管理)

年 卷 期：2022年第59卷第5期

核心收录：

学科分类：1205[管理学-图书情报与档案管理] 12[管理学] 120502[管理学-情报学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Key-Area Research and Development Program of Guangdong Province, China [2019B010154002] Guangdong Basic and Applied Basic Research Foundation, China [2022A1515010688] Guangdong Key Laboratory, China [2020B1212060068] National Natural Science Foundation of China 

主　　题：Multi-view learning Semi-supervised learning Orthogonal constraint Orthogonal non-negative matrix factorization Consensus 

摘      要：Semi-supervised multi-view learning has recently achieved appealing performance with the consensus relation between samples. However, in addition to the relation between samples, the relation between samples and their assemble centroid is also important to the learning. In this paper, we propose a novel model based on orthogonal non-negative matrix factorization, which allows exploring both the consensus relations between samples and between samples and their assemble centroid. Since this model utilizes more consensus information to guide the multi-view learning, it can lead to better performance. Meanwhile, we theoretically derive a proposition about the equivalency between the partial orthogonality and the full orthogonality. Based on this proposition, the orthogonality constraint and the label constraint are simultaneously implemented in the proposed model. Experimental evaluations on five real-world datasets show that our approach outperforms the state-of-the-art methods, where the improvement is 6% average in terms of ARI index.