Double Least Squares Pursuit for Sparse Decomposition
Wanyi Li; Peng Wang; Hong Qiao
Source PublicationIntelligent Information Processing VI
; Sparse decomposition has been widely used in numerous applications, such as image processing, pattern recognition, remote sens-ing and computational biology. Despite plenty of theoretical develop-ments have been proposed, developing, implementing and analyzing novel fast sparse approximation algorithm is still an open problem. In this paper, a new pursuit algorithm Double Least Squares Pursuit (DLSP) is proposed for sparse decomposition. In this algorithm, the support of the solution is obtained by sorting the coefficients which are calculated by the first Least-Squares, and then the non-zero values over this sup-port are detected by the second Least-Squares. The results of numerical experiment demonstrate the effectiveness of the proposed method, which is with less time complexity, more simple form, and gives close or even better performance compared to the classical Orthogonal Matching Pursuit (OMP) method.
KeywordSparse Decomposition Sparse Representation Sparse Approximation Algorithm Double Least-squares Pursuit.
Document Type期刊论文
Corresponding AuthorWanyi Li
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wanyi Li,Peng Wang,Hong Qiao. Double Least Squares Pursuit for Sparse Decomposition[J]. Intelligent Information Processing VI,2012(no):357-363.
APA Wanyi Li,Peng Wang,&Hong Qiao.(2012).Double Least Squares Pursuit for Sparse Decomposition.Intelligent Information Processing VI(no),357-363.
MLA Wanyi Li,et al."Double Least Squares Pursuit for Sparse Decomposition".Intelligent Information Processing VI .no(2012):357-363.
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