A novel dictionary learning method for sparse representation with nonconvex regularizations

In dictionary learning, sparse regularization is used to promote sparsity and has played a major role in the developing of dictionary learning algorithms. ℓ1-norm is of the most popular sparse regularization due to its convexity and the related tractable convex optimization problems. However, ℓ1-nor...

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Vydáno v:	Neurocomputing (Amsterdam) Ročník 417; s. 128 - 141
Hlavní autoři:	Tan, Benying, Li, Yujie, Zhao, Haoli, Li, Xiang, Ding, Shuxue
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 05.12.2020
Témata:	DC programming and DCA Dictionary learning Forward-backward splitting algorithm GMC regularization Nonconvex DC programming and DCA Forward-backward splitting algorithm Nonconvex GMC regularization Dictionary learning
ISSN:	0925-2312, 1872-8286
On-line přístup:	Získat plný text
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