Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

作     者：Guosheng Cui Ye Li Jianzhong Li Jianping Fan 

作者机构：Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen 518055China University of Chinese Academy of SciencesBeijing 100049China School of Computer Science and Control EngineeringShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China 

出 版 物：《Big Data Mining and Analytics》 (大数据挖掘与分析（英文）)

年 卷 期：2024年第7卷第1期

页      面：55-74页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the National Key Research and Development Project of China(No.2019YFB2102500) the Strategic Priority CAS Project(No.XDB38040200) the National Natural Science Foundation of China(Nos.62206269,U1913210) the Guangdong Provincial Science and Technology Projects(Nos.2022A1515011217,2022A1515011557) the Shenzhen Science and Technology Projects(No.JSGG20211029095546003) 

主　　题：multi-view semi-supervised clustering discriminative information geometric information feature normalizing strategy 

摘      要：Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern *** has been widely used and studied in the multi-view clustering tasks because of its *** study proposes a general semi-supervised multi-view nonnegative matrix factorization *** algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different *** specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is *** on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.