Spectral feature selection for supervised and unsupervised learning

ICML 2007
Pages: 1151 - 1157
Published: Jun 20, 2007
Abstract
Feature selection aims to reduce dimensionality for building comprehensible learning models with good generalization performance. Feature selection algorithms are largely studied separately according to the type of learning: supervised or unsupervised. This work exploits intrinsic properties underlying supervised and unsupervised feature selection algorithms, and proposes a unified framework for feature selection based on spectral graph theory....
Paper Details
Title
Spectral feature selection for supervised and unsupervised learning
Published Date
Jun 20, 2007
Journal
Pages
1151 - 1157
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