l2,1-norm regularized discriminative feature selection for unsupervised learning

Yang, Yi, Shen, Heng Tao, Ma, Zhigang, Huang, Zi and Zhou, Xiaofang (2011). l2,1-norm regularized discriminative feature selection for unsupervised learning. In: Toby Walsh, Proceedings of the 22nd International Joint Conference on Artificial Intelligence. International Joint Conference on Artificial Intelligence - IJCAI 2011, Barcelona, Catalonia, Spain, (1589-1594). 16-22 July 2011.

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Name Description MIMEType Size Downloads
Author Yang, Yi
Shen, Heng Tao
Ma, Zhigang
Huang, Zi
Zhou, Xiaofang
Title of paper l2,1-norm regularized discriminative feature selection for unsupervised learning
Formatted title
l2,1-norm regularized discriminative feature selection for unsupervised learning
Conference name International Joint Conference on Artificial Intelligence - IJCAI 2011
Conference location Barcelona, Catalonia, Spain
Conference dates 16-22 July 2011
Proceedings title Proceedings of the 22nd International Joint Conference on Artificial Intelligence
Place of Publication Menlo Park, CA, USA
Publisher AAAI Press/International Joint Conferences on Artificial Intelligence
Publication Year 2011
Sub-type Fully published paper
Open Access Status
ISBN 9781577355120
9781577355137
9781577355144
9781577355151
9781577355168
Editor Toby Walsh
Start page 1589
End page 1594
Total pages 6
Collection year 2012
Language eng
Formatted Abstract/Summary
Compared with supervised learning for feature selection, it is much more difficult to select the discriminative features in unsupervised learning due to the lack of label information. Traditional unsupervised feature selection algorithms usually select the features which best preserve the data distribution, e.g., manifold structure, of the whole feature set. Under the assumption that the class label of input data can be predicted by a linear classifier, we incorporate discriminative analysis and I2,1-norm minimization into a joint framework for unsupervised feature selection. Different from existing unsupervised feature selection algorithms, our algorithm selects the most discriminative feature subset from the whole feature set in batch mode. Extensive experiment on different data types demonstrates the effectiveness of our algorithm.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Mon, 15 Aug 2011, 17:19:12 EST by Dr Heng Tao Shen on behalf of School of Information Technol and Elec Engineering