SPARSITY

作品数：142被引量：233H指数：7
导出分析报告
相关领域：自动化与计算机技术更多>>
相关作者：李国发张若愚赵洪林孙赞东谢会文更多>>
相关机构：中国石油大学(北京)中国科学院上海交通大学中国石油更多>>
相关期刊：更多>>
相关基金：国家自然科学基金国家重点基础研究发展计划中国博士后科学基金福建省自然科学基金更多>>
-
在结果中检索

检索结果分析

结果分析中...
选择条件：
  • 期刊=Journal of the Operations Research Society of Chinax
共 条 记 录，以下是1-9
全选清除导出
参考文献引证文献引用追踪
视图：
排序：
Trace Lasso Regularization for Adaptive Sparse Canonical Correlation Analysis via Manifold Optimization Approach
《Journal of the Operations Research Society of China》2024年第3期573-599,共27页Kang-Kang Deng Zheng Peng 
supported by the National Science Foundation of China(No.12071398);the Natural Science Foundation of Hunan Province(No.2020JJ4567);the Key Scientific Research Found of Hunan Education Department(Nos.20A097 and 18A351).
Canonical correlation analysis(CCA)describes the relationship between two sets of variables by finding a linear combination that maximizes the correlation coefficient.However,in high-dimensional settings where the num...
关键词：Canonical correlation analysis Sparsity of canonical vectors Trace Lasso regularization Manifold optimization 
Binary Random Projections with Controllable Sparsity Patterns
《Journal of the Operations Research Society of China》2022年第3期507-528,共22页Wen-Ye Li Shu-Zhong Zhang 
partially supported by Guangdong Fundamental Research Fund(No.2021A1515011825);Shenzhen Fundamental Research Fund(No.KQJSCX20170728162302784).
Random projection is often used to project higher-dimensional vectors onto a lower-dimensional space,while approximately preserving their pairwise distances.It has emerged as a powerful tool in various data processing...
关键词：Binary random projection SPARSITY Dimensionality 
Nonuniqueness of Solutions of a Class of0-minimization Problems
《Journal of the Operations Research Society of China》2021年第4期893-908,共16页Jia-Liang Xu 
Recently,finding the sparsest solution of an underdetermined linear system has become an important request in many areas such as compressed sensing,image processing,statistical learning,and data sparse approximation.I...
关键词：0-minimization SPARSITY NONUNIQUENESS BOUNDEDNESS 
Alternating Direction Method of Multipliers for l_(1)-l_(2)-Regularized Logistic Regression Model
《Journal of the Operations Research Society of China》2016年第2期243-253,共11页Yan-Qin Bai Kai-Ji Shen 
the National Natural Science Foundation of China(No.11371242)。
Logistic regression has been proved as a promising method for machine learning,which focuses on the problem of classification.In this paper,we present anl_(1)-l_(2)-regularized logistic regression model,where thel1-no...
关键词：Classification problems Logistic regression model SPARSITY ALTERNATING direction method of multipliers 
On Solutions of Sparsity Constrained Optimizatio被引量：4
《Journal of the Operations Research Society of China》2015年第4期421-439,共19页Li-Li Pan Nai-Hua Xiu Sheng-Long Zhou 
supported in part by the National Natural Science Foundation of China(Nos.11431002,71271021).
In this paper,we mainly study the existence of solutions to sparsity constrained optimization(SCO).Based on the expressions of tangent cone and normal cone of sparsity constraint,we present and characterize two first-...
关键词：Sparsity constrained optimization Tangent cone Normal cone First-order optimality condition Second-order optimality condition 
The First-Order Necessary Conditions for Sparsity Constrained Optimization
《Journal of the Operations Research Society of China》2015年第4期521-535,共15页Xue Li Wen Song 
supported by the National Natural Sciences Foundation of China(No.11371116);Innovation Foundation for Graduate Students of Harbin Normal University(No.HSDSSCX2015-28).
In this paper,we study optimization problems with the sparsity constraints.Firstly we give the expressions of the Mordukhovich(the limiting)normal cone of sparsity constraint and its intersection with a polyhedral set...
关键词：Sparsity constrained optimization Mordukhovich normal cone First-order necessary conditions 
Data-Driven Tight Frame for Multi-channel Images and Its Application to Joint Color-Depth Image Reconstruction被引量：2
《Journal of the Operations Research Society of China》2015年第2期99-115,共17页Jin Wang Jian-Feng Cai 
Jian-Feng Cai is partially supported by the National Natural Science Foundation of USA(No.DMS 1418737).
In image restoration,we usually assume that the underlying image has a good sparse approximation under a certain system.Wavelet tight frame system has been proven to be such an efficient system to sparsely approximate...
关键词：Data-driven tight frame Group sparsity Image reconstruction 
Analysis of Sparse Quasi-Newton Updates with Positive Definite Matrix Completion
《Journal of the Operations Research Society of China》2014年第1期39-56,共18页Yu-Hong Dai Nobuo Yamashita 
This work was supported by the Chinese NSF Grants(Nos.11331012 and 81173633);the China National Funds for Distinguished Young Scientists(No.11125107);the CAS Program for Cross&Coorperative Team of the Science&Technology Innovation;The authors are grateful to Professors Masao Fukushima and Ya-xiang Yuan for their warm encouragement and valuable suggestions.They also thank the two anonymous referees very much for their useful comments on an early version of this paper.
Based on the idea of maximum determinant positive definite matrix completion,Yamashita(Math Prog 115(1):1–30,2008)proposed a new sparse quasi-Newton update,called MCQN,for unconstrained optimization problems with spa...
关键词：Quasi-Newton method Large-scale problems SPARSITY Positive definite matrix completion Superlinear convergence 
Robust PCA for Ground Moving Target Indication in Wide-Area Surveillance Radar System被引量：1
《Journal of the Operations Research Society of China》2013年第1期135-153,共19页Qingna Li He Yan Leqin Wu Robert Wang 
supported by the National Science Foundation of China(No.11101410);China Postdoctoral Science Foundation(No.2011M500416).
Robust PCA has found important applications in many areas,such as video surveillance,face recognition,latent semantic indexing and so on.In this paper,we study its application in ground moving target indication(GMTI)i...
关键词：Ground moving target indication Alternating direction method Wide-area surveillance radar system Joint sparsity Matrix recovery 
全选清除导出
共1页<1>
检索报告 对象比较 聚类工具 使用帮助 返回顶部