Local Feature Selection for Data Classification

Typical feature selection methods choose an optimal global feature subset that is applied over all regions of the sample space. In contrast, in this paper we propose a novel localized feature selection (LFS) approach whereby each region of the sample space is associated with its own distinct optimiz...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 38; číslo 6; s. 1217 - 1227
Hlavní autori:	Armanfard, Narges, Reilly, James P., Komeili, Majid
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.06.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Classification Feature extraction Frequency modulation Linear programming Local Feature Selection Manifolds Pareto optimization Training classification Local feature selection linear programming
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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