fi-divergence NMF with biorthogonal regularization for data representation

doi:10.1016/j.engappai.2023.106014

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	fi-divergence NMF with biorthogonal regularization for data representation
	Yuan, Ruixue 1; Leng, Chengcai 1; Li, Bing 2; Basu, Anup 3
刊名	ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
	2023-05-01
卷号	121 页码:14
关键词	NMF fi-divergence Biorthogonal regularization Clustering
ISSN号	0952-1976
DOI	10.1016/j.engappai.2023.106014
通讯作者	Leng, Chengcai(ccleng@nwu.edu.cn)
英文摘要	Non-Negative Matrix Factorization (NMF) has become a commonly used method for data representation. Orthogonal NMF improves the clustering performance by adding orthogonal constraints to the decomposed matrices. The existing orthogonal NMF methods typically use Euclidean distance to measure the difference between before and after factorization for convenience and simplicity. However, limitations of the Euclidean distance can lead to inflexibilities. In addition, failure to consider orthogonality of the decomposed features and sparsity of the data representation can also lead to degraded performance of the algorithm. In order to overcome the above shortcomings, we propose a novel fi-divergence-based NMF with biorthogonal regularization (BO-fi NMF). Our BO-fi NMF method uses generalized fi-divergence instead of Euclidean distance to measure the similarity between matrices, and selects an appropriate fi for each type of data to obtain a more flexible way of measuring similarity. In addition, we also incorporate biorthogonal constraints into the minimized objective function, which ensures both orthogonality of the decomposed features and sparsity of the data representation. Furthermore, we use trace rather than Euclidean distance to measure the orthogonality of the decomposed matrices, which reduces execution time. Finally, clustering experiments on image datasets show that the overall clustering effect of BO-fi NMF is better than state-of-the-art methods.
资助项目	National Natural Science Foundation of China[61702251] ; Natural Science Basic Research Plan in Shaanxi Province of China[2018JM6030] ; Natural Sciences and Engineering Research Council of Canada ; Youth Academic Talent Support Program of Northwest University, China[360051900151]
WOS关键词	NONNEGATIVE MATRIX FACTORIZATION ; ROBUST ; ALGORITHMS
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000990904700001
资助机构	National Natural Science Foundation of China ; Natural Science Basic Research Plan in Shaanxi Province of China ; Natural Sciences and Engineering Research Council of Canada ; Youth Academic Talent Support Program of Northwest University, China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53275]
专题	多模态人工智能系统全国重点实验室
通讯作者	Leng, Chengcai
作者单位	1.Northwest Univ, Sch Math, Xian 710127, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Alberta, Dept Comp Sci, Edmonton, AB 628, Canada
推荐引用方式 GB/T 7714	Yuan, Ruixue,Leng, Chengcai,Li, Bing,et al. fi-divergence NMF with biorthogonal regularization for data representation[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2023,121:14.
APA	Yuan, Ruixue,Leng, Chengcai,Li, Bing,&Basu, Anup.(2023).fi-divergence NMF with biorthogonal regularization for data representation.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,121,14.
MLA	Yuan, Ruixue,et al."fi-divergence NMF with biorthogonal regularization for data representation".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 121(2023):14.