Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/72133
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Type: Conference paper
Title: On the optimality of sequential forward feature selection using class separability measure
Author: Wang, L.
Shen, C.
Hartley, R.
Citation: Proceedings of the International Conference on Digital Image Computing Techniques and Applications (DICTA'11), held in Noosa, Queensland, 6-8 December, 2011: pp.203-208
Publisher: IEEE
Publisher Place: USA
Issue Date: 2011
ISBN: 9781457720062
Conference Name: International Conference on Digital Image Computing Techniques and Applications (2011 : Noosa, Qld)
Statement of
Responsibility: 
Lei Wang, Chunhua Shen and Richard Hartley
Abstract: This paper studies sequential forward feature selection that uses the scatter-matrix-based class separability measure. We find that by adding a scale factor to each iteration of the conventional sequential selection, a sequential selection that guarantees the global optimum can be attained. We give a thorough theoretical proof of its optimality via a novel geometric interpretation, and this leads to a unified framework including the optimal sequential selection, the conventional sequential selection and the best-individual-N selection. In addition, we show that with our formulation, feature selection can be treated as a linear fractional maximization problem, and it can be efficiently solved by algorithms well developed in the literature. This gives a non-sequential globally optimal feature selection algorithm. Both theoretical and experimental study demonstrate their efficiency.
Keywords: Sequential
feature selection
class separability
Rights: © 2011 IEEE
DOI: 10.1109/DICTA.2011.41
Appears in Collections:Aurora harvest
Computer Science publications

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