中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 数学与系统科学研究院
  3. 中国科学院数学与系统科学研究院
An Efficient Orthonormalization-Free Approach for Sparse Dictionary Learning and Dual Principal Component Pursuit

文献类型：期刊论文

作者Hu, Xiaoyin2,3; Liu, Xin1,2,3
刊名SENSORS
出版日期2020-06-01
卷号20期号:11页码:25
关键词dual principal component pursuit orthogonality constraint sparse dictionary learning stiefel manifold
DOI10.3390/s20113041
英文摘要Sparse dictionary learning (SDL) is a classic representation learning method and has been widely used in data analysis. Recently, the lm-norm (m >= 3,m is an element of N) maximization has been proposed to solve SDL, which reshapes the problem to an optimization problem with orthogonality constraints. In this paper, we first propose an lm-norm maximization model for solving dual principal component pursuit (DPCP) based on the similarities between DPCP and SDL. Then, we propose a smooth unconstrained exact penalty model and show its equivalence with the lm-norm maximization model. Based on our penalty model, we develop an efficient first-order algorithm for solving our penalty model (PenNMF) and show its global convergence. Extensive experiments illustrate the high efficiency of PenNMF when compared with the other state-of-the-art algorithms on solving the lm-norm maximization with orthogonality constraints.
资助项目National Natural Science Foundation of China[11971466] ; National Natural Science Foundation of China[11991021] ; National Natural Science Foundation of China[11991020] ; Key Research Program of Frontier Sciences, Chinese Academy of Sciences[ZDBS-LY-7022] ; National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences ; Youth Innovation Promotion Association, Chinese Academy of Sciences
WOS研究方向Chemistry ; Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:000552737900034
出版者MDPI
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/51900]  
专题中国科学院数学与系统科学研究院
通讯作者Hu, Xiaoyin
作者单位1.State Key Lab Sci & Engn Comp, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Hu, Xiaoyin,Liu, Xin. An Efficient Orthonormalization-Free Approach for Sparse Dictionary Learning and Dual Principal Component Pursuit[J]. SENSORS,2020,20(11):25.
APA Hu, Xiaoyin,&Liu, Xin.(2020).An Efficient Orthonormalization-Free Approach for Sparse Dictionary Learning and Dual Principal Component Pursuit.SENSORS,20(11),25.
MLA Hu, Xiaoyin,et al."An Efficient Orthonormalization-Free Approach for Sparse Dictionary Learning and Dual Principal Component Pursuit".SENSORS 20.11(2020):25.

入库方式： OAI收割

来源：数学与系统科学研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。