Sparse learning via Boolean relaxations

We introduce novel relaxations for cardinality-constrained learning problems, including least-squares regression as a special but important case. Our approach is based on reformulating a cardinality-constrained problem exactly as a Boolean program, to which standard convex relaxations such as the La...

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Veröffentlicht in:	Mathematical programming Jg. 151; H. 1; S. 63 - 87
Hauptverfasser:	Pilanci, Mert, Wainwright, Martin J., El Ghaoui, Laurent
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2015 Springer Nature B.V
Schlagworte:	Artificial intelligence Boolean Calculus of Variations and Optimal Control; Optimization Combinatorics Control theory Electrical engineering Full Length Paper Machine learning Mathematical and Computational Physics Mathematical Methods in Physics Mathematics Mathematics and Statistics Mathematics of Computing Numerical Analysis Optimization Sparsity Studies Support vector machines Theoretical United States > US 68T05 Learning and adaptive systems 90C25 Convex programming 90C09 Boolean programming Sparsity 90C06 Large-scale problems Machine learning Convex relaxation Combinatorial optimization Regularization
ISSN:	0025-5610, 1436-4646
Online-Zugang:	Volltext
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