Mutual information inspired feature selection using kernel canonical correlation analysis

Wang, Y., Cang, S. ORCID: 0000-0002-7984-0728 and Yu, H., 2019. Mutual information inspired feature selection using kernel canonical correlation analysis. Expert Systems with Applications: X, 4: 100014. ISSN 2590-1885

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This paper proposes a filter-based feature selection method by combining the measurement of kernel canonical correlation analysis (KCCA) with the mutual information (MI)-based feature selection method, named mRMJR-KCCA. The mRMJR-KCCA maximizes the relevance between the feature candidate and the target class labels and simultaneously minimizes the joint redundancy between the feature candidate and the already selected features in the view of KCCA. To improve the computation efficiency, we adopt the Incomplete Cholesky Decomposition to approximate the kernel matrix in implementing the KCCA in mRMJR-KCCA for larger-size datasets. The proposed method is experimentally evaluated on 13 classification-associated datasets. Compared with certain popular feature selection methods, the experimental results demonstrate the better performance of the proposed mRMJR-KCCA.

Item Type: Journal article
Publication Title: Expert Systems with Applications: X
Creators: Wang, Y., Cang, S. and Yu, H.
Publisher: Elsevier
Date: November 2019
Volume: 4
ISSN: 2590-1885
S2590188519300149Publisher Item Identifier
Rights: © 2019 published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( ).
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 26 Aug 2020 12:42
Last Modified: 31 May 2021 15:17

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