A convex formulation for semi-supervised multi-label feature selection

Chang, Xiaojun, Nie, Feiping, Yang, Yi and Huang, Heng (2014). A convex formulation for semi-supervised multi-label feature selection. In: AAAI Conference on Artificial Intelligence, AAAI 2014, and the 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014, Québec City, QC, Canada, (1171-1177). 27-31 July 2014.

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Name Description MIMEType Size Downloads
Author Chang, Xiaojun
Nie, Feiping
Yang, Yi
Huang, Heng
Title of paper A convex formulation for semi-supervised multi-label feature selection
Conference name AAAI Conference on Artificial Intelligence, AAAI 2014, and the 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
Conference location Québec City, QC, Canada
Conference dates 27-31 July 2014
Convener AAAI
Journal name Proceedings of Twenty-Eighth AAAI Conference on Artificial Intelligence, The Twenty-Sixth Innovative Applications of Artificial Intelligence Conference, The 5th Symposium on Educational Advances in Artificial Intelligence   Check publisher's open access policy
Place of Publication Palo Alto, CA, United States
Publisher AAAI Press
Publication Year 2014
Sub-type Fully published paper
Open Access Status
ISBN 9781577356783
ISSN 2159-5399
Volume 2
Start page 1171
End page 1177
Total pages 7
Collection year 2015
Abstract/Summary Explosive growth of multimedia data has brought challenge of how to efficiently browse, retrieve and organize these data. Under this circumstance, different approaches have been proposed to facilitate multimedia analysis. Several semi-supervised feature selection algorithms have been proposed to exploit both labeled and unlabeled data. However, they are implemented based on graphs, such that they cannot handle large-scale datasets. How to conduct semi-supervised feature selection on large-scale datasets has become a challenging research problem. Moreover, existing multi-label feature selection algorithms rely on eigen-decomposition with heavy computational burden, which further prevent current feature selection algorithms from being applied for big data. In this paper, we propose a novel convex semi-supervised multi-label feature selection algorithm, which can be applied to large-scale datasets. We evaluate performance of the proposed algorithm over five benchmark datasets and compare the results with state-of-the-art supervised and semi-supervised feature selection algorithms as well as baseline using all features. The experimental results demonstrate that our proposed algorithm consistently achieve superiors performances.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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