DCA based approaches for bi-level variable selection and application for estimate multiple sparse covariance matrices

•Two new models via new regularizations for bi-level variable selection are considered.•The first uses the mixed zero norm and the second is the combined ℓ0+ℓq,0 norm.•All the resulting optimization problems are formulated as DC programs.•DCA are investigated via suitable DC decompositions for these...

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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 466; S. 162 - 177
Hauptverfasser:	Le Thi, Hoai An, Phan, Duy Nhat, Pham Dinh, Tao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 27.11.2021 Elsevier
Schlagworte:	Bi-level variable selection Computer Science DC programming DCA Mixed norm Multiple covariance matrices Nonconvex approximation DC programming Bi-level variable selection Multiple covariance matrices Nonconvex approximation DCA Mixed norm
ISSN:	0925-2312, 1872-8286
Online-Zugang:	Volltext
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