Unsupervised feature selection using nonnegative spectral analysis

Li, Zechao, Yang, Yi, Liu, Jing, Zhou, Xiaofang and Lu, Hanqing (2012). Unsupervised feature selection using nonnegative spectral analysis. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence. Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), Toronto, Canada, (1026-1032). 22-26 July 2012.

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Name Description MIMEType Size Downloads
Author Li, Zechao
Yang, Yi
Liu, Jing
Zhou, Xiaofang
Lu, Hanqing
Title of paper Unsupervised feature selection using nonnegative spectral analysis
Conference name Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)
Conference location Toronto, Canada
Conference dates 22-26 July 2012
Proceedings title Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence
Journal name Proceedings of the National Conference on Artificial Intelligence
Place of Publication Menlo Park, CA, United States
Publisher AAAI Press
Publication Year 2012
Sub-type Fully published paper
Open Access Status
ISBN 9781577355687
Volume 2
Start page 1026
End page 1032
Total pages 7
Collection year 2013
Language eng
Formatted Abstract/Summary
In this paper, a new unsupervised learning algorithm, namely Nonnegative Discriminative Feature Selection (NDFS), is proposed. To exploit the discriminative information in unsupervised scenarios, we perform spectral clustering to learn the cluster labels of the input samples, during which the feature selection is performed simultaneously. The joint learning of the cluster labels and feature selection matrix enables NDFS to select the most discriminative features. To learn more accurate cluster labels, a nonnegative constraint is explicitly imposed to the class indicators. To reduce the redundant or even noisy features, /2;1-norm minimization constraint is added into the objective function, which guarantees the feature selection matrix sparse in rows. Our algorithm exploits the discriminative information and feature correlation simultaneously to select a better feature subset. A simple yet efficient iterative algorithm is designed to optimize the proposed objective function. Experimental results on different real world datasets demonstrate the encouraging performance of our algorithm over the state-of-the-arts.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Fri, 19 Apr 2013, 16:31:30 EST by Ms Deborah Brian on behalf of School of Information Technol and Elec Engineering