Structured Sparsity Optimization With Non-Convex Surrogates of l2,0-Norm : A Unified Algorithmic Framework
In this article, we present a general optimization framework that leverages structured sparsity to achieve superior recovery results. The traditional method for solving the structured sparse objectives based on l2,0-norm is to use the l2,1-norm as a convex surrogate. However, such an approximation o...
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Détails bibliographiques
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 45(2023), 5 vom: 08. Mai, Seite 6386-6402
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| Auteur principal: |
Zhang, Xiaoqin
(Auteur) |
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| Autres auteurs: |
Zheng, Jingjing,
Wang, Di,
Tang, Guiying,
Zhou, Zhengyuan,
Lin, Zhouchen |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2023
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence
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| Sujets: | Journal Article |
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