Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/92649
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Type: Journal article
Title: A unified feature selection framework for graph embedding on high dimensional data
Author: Chen, M.
Tsang, I.
Tan, M.
Cham, T.
Citation: IEEE Transactions on Knowledge and Data Engineering, 2015; 27(6):1465-1477
Publisher: IEEE
Issue Date: 2015
ISSN: 1041-4347
1558-2191
Statement of
Responsibility: 
Marcus Chen, Ivor W. Tsang, Mingkui Tan, and Tat Jen Cham
Abstract: Although graph embedding has been a powerful tool for modeling data intrinsic structures, simply employing all features for data structure discovery may result in noise amplification. This is particularly severe for high dimensional data with small samples. To meet this challenge, this paper proposes a novel efficient framework to perform feature selection for graph embedding, in which a category of graph embedding methods is cast as a least squares regression problem. In this framework, a binary feature selector is introduced to naturally handle the feature cardinality in the least squares formulation. The resultant integral programming problem is then relaxed into a convex Quadratically Constrained Quadratic Program (QCQP) learning problem, which can be efficiently solved via a sequence of accelerated proximal gradient (APG) methods. Since each APG optimization is w.r.t. only a subset of features, the proposed method is fast and memory efficient. The proposed framework is applied to several graph embedding learning problems, including supervised, unsupervised, and semi-supervised graph embedding. Experimental results on several high dimensional data demonstrated that the proposed method outperformed the considered state-of-the-art method.
Keywords: Sparse graph embedding; sparse principal component analysis; efficient feature selection; high dimensional data
Rights: © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
DOI: 10.1109/TKDE.2014.2382599
Grant ID: http://purl.org/au-research/grants/arc/FT130100746
Appears in Collections:Aurora harvest 2
Computer Science publications

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