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Feature selection in high-dimensional classification

Author(s): Kolar, Mladen; Liu, Han

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dc.contributor.authorKolar, Mladen-
dc.contributor.authorLiu, Han-
dc.date.accessioned2020-04-06T16:24:22Z-
dc.date.available2020-04-06T16:24:22Z-
dc.date.issued2013-01-01en_US
dc.identifier.citationKolar, M, Liu, H. (2013). Feature selection in high-dimensional classification. 30th International Conference on Machine Learning, ICML 2013, PART 1), 329 - 337en_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1br45-
dc.description.abstractHigh-dimensional discriminant analysis is of fundamental importance in multivariate statistics. Existing theoretical results sharply characterize different procedures, providing sharp convergence results for the classification risk, as well as the ℓ2 convergence results to the discriminative rule. However, sharp theoretical results for the problem of variable selection have not been established, even though model interpretation is of importance in many scientific domains. In this paper, we bridge this gap by providing sharp sufficient conditions for consistent variable selection using the ROAD estimator (Fan et al., 2010). Our results provide novel theoretical insights for the ROAD estimator. Sufficient conditions are complemented by the necessary information theoretic limits on variable selection in high-dimensional discriminant analysis. This complementary result also establishes optimality of the ROAD estimator for a certain family of problems. Copyright 2013 by the author(s).en_US
dc.format.extent329 - 337en_US
dc.language.isoen_USen_US
dc.relation.ispartof30th International Conference on Machine Learning, ICML 2013en_US
dc.rightsFinal published version. This is an open access article.en_US
dc.titleFeature selection in high-dimensional classificationen_US
dc.typeJournal Articleen_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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