About: Structured sparsity regularization

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Structured sparsity regularization is a class of methods, and an area of research in statistical learning theory, that extend and generalize sparsity regularization learning methods. Both sparsity and structured sparsity regularization methods seek to exploit the assumption that the output variable (i.e., response, or dependent variable) to be learned can be described by a reduced number of variables in the input space (i.e., the domain, space of features or explanatory variables). Sparsity regularization methods focus on selecting the input variables that best describe the output. Structured sparsity regularization methods generalize and extend sparsity regularization methods, by allowing for optimal selection over structures like groups or networks of input variables in .

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rdfs:label	Structured sparsity regularization (en)
rdfs:comment	Structured sparsity regularization is a class of methods, and an area of research in statistical learning theory, that extend and generalize sparsity regularization learning methods. Both sparsity and structured sparsity regularization methods seek to exploit the assumption that the output variable (i.e., response, or dependent variable) to be learned can be described by a reduced number of variables in the input space (i.e., the domain, space of features or explanatory variables). Sparsity regularization methods focus on selecting the input variables that best describe the output. Structured sparsity regularization methods generalize and extend sparsity regularization methods, by allowing for optimal selection over structures like groups or networks of input variables in . (en)
dcterms:subject	First order methods Machine learning Convex optimization
Wikipage page ID	48844125 (xsd:integer)
Wikipage revision ID	1101441131 (xsd:integer)
Link from a Wikipage to another Wikipage	Curse of dimensionality Dependent and independent variables Proximal gradient methods for learning Convex analysis Convex function Basis pursuit Magnetic resonance imaging Signal processing Compressed sensing Empirical risk minimization First order methods Feature (machine learning) Feature selection Matching pursuit Machine learning Time complexity Domain of a function Lasso (statistics) Facial recognition system Convex optimization Regularization (mathematics) Stepwise regression Latent variable model Differentiable function Directed acyclic graph Loss function Multiple kernel learning Sociolinguistics Computational learning theory Unsupervised learning Proximal gradient method Topic model Sparse approximation Submodular set function
sameAs	Structured sparsity regularization Structured sparsity regularization Structured sparsity regularization
dbp:wikiPageUsesTemplate	dbt:Main_article dbt:Orphan dbt:Reflist
has abstract	Structured sparsity regularization is a class of methods, and an area of research in statistical learning theory, that extend and generalize sparsity regularization learning methods. Both sparsity and structured sparsity regularization methods seek to exploit the assumption that the output variable (i.e., response, or dependent variable) to be learned can be described by a reduced number of variables in the input space (i.e., the domain, space of features or explanatory variables). Sparsity regularization methods focus on selecting the input variables that best describe the output. Structured sparsity regularization methods generalize and extend sparsity regularization methods, by allowing for optimal selection over structures like groups or networks of input variables in . Common motivation for the use of structured sparsity methods are model interpretability, high-dimensional learning (where dimensionality of may be higher than the number of observations ), and reduction of computational complexity. Moreover, structured sparsity methods allow to incorporate prior assumptions on the structure of the input variables, such as overlapping groups, non-overlapping groups, and acyclic graphs. Examples of uses of structured sparsity methods include face recognition, magnetic resonance image (MRI) processing, socio-linguistic analysis in natural language processing, and analysis of genetic expression in breast cancer. (en)
prov:wasDerivedFrom	wikipedia-en:Structured_sparsity_regularization?oldid=1101441131&ns=0
page length (characters) of wiki page	24663 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Structured_sparsity_regularization
is Link from a Wikipage to another Wikipage of	Best subset selection Sparsity regularization Outline of machine learning
is Wikipage redirect of	Best subset selection Sparsity regularization
is foaf:primaryTopic of	wikipedia-en:Structured_sparsity_regularization

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