NDLI: Multiclass Feature Selection With Kernel Gram-Matrix-Based Criteria

Content Provider	IEEE Xplore Digital Library
Author	Ramona, M. Richard, G. David, B.
Copyright Year	2012
Abstract	Feature selection has been an important issue in recent decades to determine the most relevant features according to a given classification problem. Numerous methods have emerged that take into account support vector machines (SVMs) in the selection process. Such approaches are powerful but often complex and costly. In this paper, we propose new feature selection methods based on two criteria designed for the optimization of SVM: kernel target alignment and kernel class separability. We demonstrate how these two measures, when fully expressed, can build efficient and simple methods, easily applicable to multiclass problems and iteratively computable with minimal memory requirements. An extensive experimental study is conducted both on artificial and real-world datasets to compare the proposed methods to state-of-the-art feature selection algorithms. The results demonstrate the relevance of the proposed methods both in terms of performance and computational cost.
Page Count	13
File Size	609672
Starting Page	1611
Ending Page	1623
File Format	PDF
ISSN	2162237X
Volume Number	23
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Support vector machines Optimization Training Matrix decomposition Vectors Synthetic aperture sonar variable selection Audio classification feature selection kernel class separability kernel target alignment (KTA) support vector machines (SVMs)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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