Group-Sparse SVD Models via L-1- and L-0-norm Penalties and their Applications in Biological Data

doi:10.1109/TKDE.2019.2932063

CSpace > 应用数学研究所

	Group-Sparse SVD Models via L-1- and L-0-norm Penalties and their Applications in Biological Data
	Min, Wenwen 1,2; Liu, Juan 3; Zhang, Shihua4,5,6
	2021-02-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	33 期号:2 页码:536-550
摘要	Sparse Singular Value Decomposition (SVD) models have been proposed for biclustering high dimensional gene expression data to identify block patterns with similar expressions. However, these models do not take into account prior group effects upon variable selection. To this end, we first propose group-sparse SVD models with group Lasso (GL(1)-SVD) and group L-0-norm penalty (GL(0)-SVD) for non-overlapping group structure of variables. However, such group-sparse SVD models limit their applicability in some problems with overlapping structure. Thus, we also propose two group-sparse SVD models with overlapping group Lasso (OGL(1)-SVD) and overlapping group L-0-norm penalty (OGL(0)-SVD). We first adopt an alternating iterative strategy to solve GL(1)-SVD based on a block coordinate descent method, and GL(0)-SVD based on a projection method. The key of solving OGL(1)-SVD is a proximal operator with overlapping group Lasso penalty. We employ an alternating direction method of multipliers (ADMM) to solve the proximal operator. Similarly, we develop an approximate method to solve OGL(0)-SVD. Applications of these methods and comparison with competing ones using simulated data demonstrate their effectiveness. Extensive applications of them onto several real gene expression data with gene prior group knowledge identify some biologically interpretable gene modules.
关键词	Sparse SVD low-rank matrix decomposition group-sparse penalty overlapping group-sparse penalty coordinate descent method alternating direction method of multipliers (ADMM) data mining
DOI	10.1109/TKDE.2019.2932063
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61802157] ; National Natural Science Foundation of China[11661141019] ; National Natural Science Foundation of China[61621003] ; National Natural Science Foundation of China[61422309] ; National Natural Science Foundation of China[61379092] ; Strategic Priority Research Pro-gram of the Chinese Academy of Sciences (CAS)[XDB13040600] ; National Ten Thousand Talent Pro-gram for Young Top-notch Talents ; Key Research Pro-gram of the Chinese Academy of Sciences[KFZD-SW-219] ; National Key Research and Development Program of China[2017YFC0908405] ; CAS Frontier Science Re-search Key Project for Top Young Scientist[QYZDB-SSW-SYS008] ; Frontier Project of Wuhan for Application Foundation[2019010701011381]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000607806200016
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/58007
专题	应用数学研究所
通讯作者	Liu, Juan; Zhang, Shihua
作者单位	1.Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Peoples R China 2.Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China 3.Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China 4.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, CEMS,RCSDS, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 6.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China
推荐引用方式 GB/T 7714	Min, Wenwen,Liu, Juan,Zhang, Shihua. Group-Sparse SVD Models via L-1- and L-0-norm Penalties and their Applications in Biological Data[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2021,33(2):536-550.
APA	Min, Wenwen,Liu, Juan,&Zhang, Shihua.(2021).Group-Sparse SVD Models via L-1- and L-0-norm Penalties and their Applications in Biological Data.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,33(2),536-550.
MLA	Min, Wenwen,et al."Group-Sparse SVD Models via L-1- and L-0-norm Penalties and their Applications in Biological Data".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 33.2(2021):536-550.