On the optimality of sequential forward feature selection using class separability measure

Lei Wang*, Chunhua Shen, Richard Hartley

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    13 Citations (Scopus)

    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.

    Original languageEnglish
    Title of host publicationProceedings - 2011 International Conference on Digital Image Computing
    Subtitle of host publicationTechniques and Applications, DICTA 2011
    Pages203-208
    Number of pages6
    DOIs
    Publication statusPublished - 2011
    Event2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011 - Noosa, QLD, Australia
    Duration: 6 Dec 20118 Dec 2011

    Publication series

    NameProceedings - 2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011

    Conference

    Conference2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011
    Country/TerritoryAustralia
    CityNoosa, QLD
    Period6/12/118/12/11

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