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A multiple sequential orthogonal least squares algorithm for feature ranking and subset selection

Billings, S.A. and Wei, H.L. (2005) A multiple sequential orthogonal least squares algorithm for feature ranking and subset selection. Research Report. ACSE Research Report no. 908 . Automatic Control and Systems Engineering, University of Sheffield

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Abstract

High-dimensional data analysis involving a large number of variables or features is commonly encountered in multiple regression and multivariate pattern recognition. It has been noted that in many cases not all the original variables are necessary for characterizing the overall features. More often only a subset of a small number of significant variables is required. The detection of significant variables from a library consisting of all the original variables is therefore a key and challenging step for dimensionality reduction. Principal component analysis is a useful tool for dimensionality reduction. Principal components, however, suffer from two main deficiencies: Principal components always involve all the original variables and are usually difficult to physically interpret. This study introduces a new multiple sequential orthogonal least squares algorithm for feature ranking and subset selection. The new method detects in a stepwise way the capability of each candidate feature to recover the first few principal components. At each step, only the significant variable with the strongest capability to represent the first few principal components is selected. Unlike principal components, which carry no clear physical meanings, features selected by the new method preserve the original measurement meanings.

Item Type: Monograph (Research Report)
Copyright, Publisher and Additional Information: The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
Keywords: Dimensionality reduction, high-dimensional data analysis, subset selection, principal component analysis, orthogonal least squares.
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) > ACSE Research Reports
Depositing User: Miss Anthea Tucker
Date Deposited: 21 Sep 2012 15:35
Last Modified: 10 Jun 2014 16:27
Status: Published
Publisher: Automatic Control and Systems Engineering, University of Sheffield
URI: http://eprints.whiterose.ac.uk/id/eprint/74508

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