.png){kind=icon}
.png){kind=icon}
Return
A feature selection method based on kernel canonical correlation analysis and the minimum Redundancy-Maximum Relevance filter method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2011.09.031.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Feature selection
Mutual information
KCCA
mRMR
Relevant redundancy
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
View more organizations