期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 39, 期 3, 页码 3432-3437出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.09.031
关键词
Feature selection; Mutual information; KCCA; mRMR; Relevant redundancy
类别
资金
- Turkish Scientific Technical Research Council (TUBITAK) [2211]
- Scientific Research Projects Coordination Unit of Istanbul University [YADOP-5323]
In this paper, we propose a feature selection method based on a recently popular minimum Redundancy-Maximum Relevance (mRMR) criterion, which we called Kernel Canonical Correlation Analysis based mRMR (KCCAmRMR) based on the idea of finding the unique information, i.e. information that is distinct from the set of already selected variables, that a candidate variable possesses about the target variable. In simplest terms, for this purpose, we propose using correlated functions explored by KCCA instead of using the features themselves as inputs to mRMR. We demonstrate the usefulness of our method on both toy and benchmark datasets. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.
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