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

作　　者：Guosheng Cui Ye Li Jianzhong Li Jianping Fan 

机构地区：[1]Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China [2]Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology,Shenzhen 518055,China [3]University of Chinese Academy of Sciences,Beijing 100049,China [4]School of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China

出　　处：《Big Data Mining and Analytics》2024年第1期55-74,共20页大数据挖掘与分析（英文）

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)。

摘　　要：Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern recognition.It has been widely used and studied in the multi-view clustering tasks because of its effectiveness.This study proposes a general semi-supervised multi-view nonnegative matrix factorization algorithm.This algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different views.Two 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 presented.Experiments on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.

关 键 词：MULTI-VIEW semi-supervised clustering discriminative information geometric information feature normalizing strategy 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]