On Equivalence of l1 Norm Based Basic Sparse Representation Problems
Jiang, Rui2; Qiao, Hong2; Zhang, Bo1; Jiang R(姜锐)
2015-09
Conference NameIEEE Conference of Signal Processing, Communications and Computing (ICSPCC), 2015
Source PublicationProceedings of IEEE Conference of Signal Processing, Communications and Computing (ICSPCC), 2015
Conference DateSeptember 19-22, 2015
Conference PlaceNingbo, Zhejiang, China
Abstract
The l1 norm regularization problem, the l1 norm minimization problem and the l1 norm constraint problem are known collectively as the l1 norm based Basic Sparse
Representation Problems (BSRPs), and have been popular basic models in the field of signal processing and machine learning. The equivalence of the above three problems is one of the crucial bases for the corresponding algorithms design. However, to the best our knowledge, this equivalence issue has not been addressed appropriately in the existing literature. In this paper, we will give a rigorous proof of the equivalence of the three l1 norm based BSRPs in the case when the dictionary is an overcomplete and row full rank matrix.
KeywordEquivalence L1 Norm Regularization Problem L1 Norm Minimization Problem L1 Norm Constraint Problem
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12464
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorJiang R(姜锐)
Affiliation1.Chinese Acad Sci, LSEC, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Jiang, Rui,Qiao, Hong,Zhang, Bo,et al. On Equivalence of l1 Norm Based Basic Sparse Representation Problems[C],2015.
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