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Feature selection via dependence maximization

Song, Le; Smola, Alexander; Gretton, Arthur; Bedo, Justin; Borgwardt, Karsten

Description

We introduce a framework for feature selection based on dependence maximization between the selected features and the labels of an estimation problem, using the Hilbert-Schmidt Independence Criterion. The key idea is that good features should be highly dependent on the labels. Our approach leads to a greedy procedure for feature selection. We show that a number of existing feature selectors are special cases of this framework. Experiments on both artificial and real-world data show that our...[Show more]

CollectionsANU Research Publications
Date published: 2012
Type: Journal article
URI: http://hdl.handle.net/1885/62622
Source: Journal of Machine Learning Research

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