About: Proximal gradient methods for learning

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Proximal gradient (forward backward splitting) methods for learning is an area of research in optimization and statistical learning theory which studies algorithms for a general class of convex regularization problems where the regularization penalty may not be differentiable. One such example is regularization (also known as Lasso) of the form

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rdfs:label	Proximal gradient methods for learning (en)
rdfs:comment	Proximal gradient (forward backward splitting) methods for learning is an area of research in optimization and statistical learning theory which studies algorithms for a general class of convex regularization problems where the regularization penalty may not be differentiable. One such example is regularization (also known as Lasso) of the form (en)
dcterms:subject	First order methods Machine learning Convex optimization
Wikipage page ID	41200806 (xsd:integer)
Wikipage revision ID	1117682944 (xsd:integer)
Link from a Wikipage to another Wikipage	Elastic net regularization Proximal operator Convex analysis Convex optimization Gradient Gradient descent Convex conjugate Convex function Lipschitz continuity Empirical risk minimization First order methods Ball (mathematics) Machine learning Dual norm Lasso (statistics) Convex optimization Projection (linear algebra) Regularization (mathematics) Hilbert space L1-norm Thresholding (image processing) Differentiable function Directed acyclic graph Optimization Semicontinuous function Computational learning theory Semi-continuity Proximal gradient method Subdifferential
sameAs	Proximal gradient methods for learning Proximal gradient methods for learning Proximal gradient methods for learning Proximal gradient methods for learning
dbp:wikiPageUsesTemplate	dbt:Reflist
has abstract	Proximal gradient (forward backward splitting) methods for learning is an area of research in optimization and statistical learning theory which studies algorithms for a general class of convex regularization problems where the regularization penalty may not be differentiable. One such example is regularization (also known as Lasso) of the form Proximal gradient methods offer a general framework for solving regularization problems from statistical learning theory with penalties that are tailored to a specific problem application. Such customized penalties can help to induce certain structure in problem solutions, such as sparsity (in the case of lasso) or group structure (in the case of group lasso). (en)
prov:wasDerivedFrom	wikipedia-en:Proximal_gradient_methods_for_learning?oldid=1117682944&ns=0
page length (characters) of wiki page	20337 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Proximal_gradient_methods_for_learning
is Link from a Wikipage to another Wikipage of	Proximal gradient Least squares Christine De Mol Multiple kernel learning Computational learning theory Proximal gradient method Outline of machine learning Structured sparsity regularization Projected gradient descent
is Wikipage redirect of	Proximal gradient Projected gradient descent
is foaf:primaryTopic of	wikipedia-en:Proximal_gradient_methods_for_learning

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