An algorithm of nonnegative matrix factorization under structure constraints for image clustering

被引：0

作者：

Jia, Mengxue ^{[1
,2
]}

Li, Xiangli ^{[1
,3
,4
]}

Zhang, Ying ^{[1
,2
]}

机构：

[1] Guilin Univ Elect Technol, Sch Math & Comp Sci, Guilin 541004, Guangxi, Peoples R China

[2] Xidian Univ, Sch Math & Stat, Xian 710126, Shaanxi, Peoples R China

[3] Guilin Univ Elect Technol, Guangxi Coll & Univ Key Lab Data Anal & Computat, Guilin 541004, Guangxi, Peoples R China

[4] Ctr Appl Math Guangxi GUET, Guilin 541004, Guangxi, Peoples R China

来源：

NEURAL COMPUTING & APPLICATIONS | 2023年 / 35卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Image clustering; Nonnegative matrix factorization; Cosine measure; !segment]l[!segment](2) norm; P-HARMONIC FLOWS;

D O I：

10.1007/s00521-022-08136-x

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Nonnegative matrix factorization (NMF) is a crucial method for image clustering. However, NMF may obtain low accurate clustering results because the factorization results contain no data structure information. In this paper, we propose an algorithm of nonnegative matrix factorization under structure constraints (SNMF). The factorization results of SNMF could maintain data global and local structure information simultaneously. In SNMF, the global structure information is captured by the cosine measure under the l(2) norm constraints. Meanwhile, l(2) norm constraints are utilized to get more discriminant data representations. A graph regularization term is employed to maintain the local structure. Effective updating rules are given in this paper. Moreover, the effects of different normalizations on similarities are investigated through experiments. On real datasets, the numerical results confirm the effectiveness of the SNMF.

引用

页码：7891 / 7907

页数：17

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