2016年7月2日
Constrained analysis dictionary learning with the ℓ<inf>1/2</inf>-norm regularizer
International Conference on Signal Processing Proceedings, ICSP
- ,
- ,
- ,
- ,
- 開始ページ
- 890
- 終了ページ
- 894
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/ICSP.2016.7877958
© 2016 IEEE. Sparse representation has been proven to be a powerful tool for analysis and processing of signals and images. Whereas the most existing sparse representation methods are based on the synthesis model, this paper addresses sparse representation with the so-called analysis model. The ℓ1/2-norm regularizer theory in compressive sensing (CS) shows that the ℓ1/2-norm regularizer can yield stronger sparsity-promoting solutions than the ℓ1-norm regularizer. In this paper, we propose a novel and efficient algorithm for analysis dictionary learning problem with ℓ1/2-norm regularizer as sparsity constraint, which includes two stages: the analysis sparse coding stage and the analysis dictionary update stage. In the analysis sparse coding stage, adaptive half-thresholding is employed to solve the ℓ1/2-norm regularizer problem. In the analysis dictionary update stage, the solution can be straightforwardly obtained by solving the related least square problem followed by a projection. According to our simulation study, the main advantage of the proposed algorithms is its greater learning efficiency in different cosparsities.
- リンク情報
-
- DOI
- https://doi.org/10.1109/ICSP.2016.7877958
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000406056300174&DestApp=WOS_CPL
- Scopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85016333547&origin=inward
- Scopus Citedby
- https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85016333547&origin=inward
- ID情報
-
- DOI : 10.1109/ICSP.2016.7877958
- ISSN : 2164-5221
- SCOPUS ID : 85016333547
- Web of Science ID : WOS:000406056300174