Improved Non-negative Matrix Factorization Algorithm for Sparse Graph Regularization  

作　　者：Caifeng Yang Tao Liu Guifu Lu Zhenxin Wang Zhi Deng 

机构地区：[1]College of Computer and Information,Anhui Polytechnic University,Wuhu,China [2]School of Computer Science,Northwestern Polytechnic University,Xi’an,China

出　　处：《国际计算机前沿大会会议论文集》2021年第1期221-232,共12页International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）

基　　金：This work was supported by the National Natural Science Foundation of China(Grant No.61501005);the Anhui Natural Science Foundation(Grant No.1608085 MF 147);the Natural Science Foundation of Anhui Universities(Grant No.KJ2016A057);the Industry Collaborative Innovation Fund of Anhui Polytechnic University and Jiujiang District(Grant No.2021cyxtb4);the Science Research Project of Anhui Polytechnic University(Grant No.Xjky2020120).

摘　　要：Aiming at the low recognition accuracy of non-negative matrix factorization(NMF)in practical application,an improved spare graph NMF(New-SGNMF)is proposed in this paper.New-SGNMF makes full use of the inherent geometric structure of image data to optimize the basis matrix in two steps.A threshold value s was first set to judge the threshold value of the decomposed base matrix to filter the redundant information in the data.Using L2 norm,sparse constraints were then implemented on the basis matrix,and integrated into the objective function to obtain the objective function of New-SGNMF.In addition,the derivation process of the algorithm and the convergence analysis of the algorithm were given.The experimental results on COIL20,PIE-pose09 and YaleB database show that compared with K-means,PCA,NMF and other algorithms,the proposed algorithm has higher accuracy and normalized mutual information.

关 键 词：Image recognition Non-negative matrix factorization Graph regularization Basis matrix Sparseness constraints 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]