Supervised Feature Selection via Dependence Estimation

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dc.creatorSong, Le
dc.creatorSmola, Alex
dc.creatorGretton, Arthur
dc.creatorBorgwardt, Karsten
dc.creatorBedo, Justin
dc.date2007-04-20
dc.date.accessioned2026-07-07T07:57:35Z
dc.date.available2026-07-07T07:57:35Z
dc.descriptionWe introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The key idea is that good features should maximise such dependence. Feature selection for various supervised learning problems (including classification and regression) is unified under this framework, and the solutions can be approximated using a backward-elimination algorithm. We demonstrate the usefulness of our method on both artificial and real world datasets.
dc.description9 pages
dc.identifierhttps://arxiv.org/abs/0704.2668
dc.identifierhttp://arxiv.org/abs/0704.2668
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/127696
dc.subjectMachine Learning
dc.titleSupervised Feature Selection via Dependence Estimation
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