UNSUPERVISED FEATURE SELECTION USING INCREMENTAL LEAST SQUARES

Rong Liu (), Robert Rallo () and Yoram Cohen ()
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Rong Liu: Center for the Environmental Implications of Nanotechnology and Chemical and Biomolecular Engineering Department, University of California, Los Angeles, CA 90095, USA
Robert Rallo: Center for the Environmental Implications of Nanotechnology and Chemical and Biomolecular Engineering Department, University of California, Los Angeles, CA 90095, USA;
Yoram Cohen: Center for the Environmental Implications of Nanotechnology and Chemical and Biomolecular Engineering Department, University of California, Los Angeles, CA 90095, USA

International Journal of Information Technology & Decision Making (IJITDM), 2011, vol. 10, issue 06, 967-987

Abstract: An unsupervised feature selection method is proposed for analysis of datasets of high dimensionality. The least square error (LSE) of approximating the complete dataset via a reduced feature subset is proposed as the quality measure for feature selection. Guided by the minimization of the LSE, a kernel least squares forward selection algorithm (KLS-FS) is developed that is capable of both linear and non-linear feature selection. An incremental LSE computation is designed to accelerate the selection process and, therefore, enhances the scalability of KLS-FS to high-dimensional datasets. The superiority of the proposed feature selection algorithm, in terms of keeping principal data structures, learning performances in classification and clustering applications, and robustness, is demonstrated using various real-life datasets of different sizes and dimensions.

Keywords: Feature selection; least squares; filter; kernel method; data mining (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1142/S0219622011004671

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