NDLI: Unsupervised discriminative feature selection in a kernel space via L-norm minimization

Content Provider	IEEE Xplore Digital Library
Author	Yang Liu Yizhou Wang
Copyright Year	2012
Description	Author affiliation: Key Lab. of Machine Perception (MoE), Peking Univ., Beijing, China (Yang Liu; Yizhou Wang)
Abstract	Traditional nonlinear feature selection methods map the data from an original space into a kernel space to make the data be separated more easily, then move back to the original feature space to select features. However, the performance of clustering or classification is better in the kernel space, so we are able to select the features directly in the kernel space and get the direct importance of each feature. Motivated by this idea, we propose a novel method for unsupervised feature selection directly in the kernel space. To do this, we utilize local discriminative information to find the best label for each instance with $L_{2,1}-norm$ minimization, then select the most important features in the kernel space using the labels predicted. Extensive experiments demonstrate the effectiveness of our method.
Starting Page	1205
Ending Page	1208
File Size	176458
Page Count	4
File Format	PDF
ISBN	9781467322164
ISSN	10514651
e-ISBN	9784990644109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-11
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	ICPR Org Committee
Subject Keyword	Kernel Accuracy Minimization Algorithm design and analysis Clustering algorithms Linear programming Single photon emission computed tomography
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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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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