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Efficient spectral feature selection with minimum redundancy

Conference Paper


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Abstract


  • Spectral feature selection identifies relevant features by

    measuring their capability of preserving sample similarity.

    It provides a powerful framework for both supervised

    and unsupervised feature selection, and has been

    proven to be effective in many real-world applications.

    One common drawback associated with most existing

    spectral feature selection algorithms is that they evaluate

    features individually and cannot identify redundant

    features. Since redundant features can have significant

    adverse effect on learning performance, it is necessary

    to address this limitation for spectral feature selection.

    To this end, we propose a novel spectral feature selection

    algorithm to handle feature redundancy, adopting

    an embedded model. The algorithm is derived from

    a formulation based on a sparse multi-output regression

    with a L2;1-norm constraint. We conduct theoretical

    analysis on the properties of its optimal solutions,

    paving the way for designing an efficient path-following

    solver. Extensive experiments show that the proposed

    algorithm can do well in both selecting relevant features

    and removing redundancy.

Authors


  •   zhao, zheng (external author)
  •   Wang, Lei
  •   Liu, Huan (external author)

Publication Date


  • 2010

Citation


  • zhao, z., Wang, L. & Liu, H. (2010). Efficient spectral feature selection with minimum redundancy. Proceedings of Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI) (pp. 1-6).

Scopus Eid


  • 2-s2.0-77958565426

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1536&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/530

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 6

Abstract


  • Spectral feature selection identifies relevant features by

    measuring their capability of preserving sample similarity.

    It provides a powerful framework for both supervised

    and unsupervised feature selection, and has been

    proven to be effective in many real-world applications.

    One common drawback associated with most existing

    spectral feature selection algorithms is that they evaluate

    features individually and cannot identify redundant

    features. Since redundant features can have significant

    adverse effect on learning performance, it is necessary

    to address this limitation for spectral feature selection.

    To this end, we propose a novel spectral feature selection

    algorithm to handle feature redundancy, adopting

    an embedded model. The algorithm is derived from

    a formulation based on a sparse multi-output regression

    with a L2;1-norm constraint. We conduct theoretical

    analysis on the properties of its optimal solutions,

    paving the way for designing an efficient path-following

    solver. Extensive experiments show that the proposed

    algorithm can do well in both selecting relevant features

    and removing redundancy.

Authors


  •   zhao, zheng (external author)
  •   Wang, Lei
  •   Liu, Huan (external author)

Publication Date


  • 2010

Citation


  • zhao, z., Wang, L. & Liu, H. (2010). Efficient spectral feature selection with minimum redundancy. Proceedings of Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI) (pp. 1-6).

Scopus Eid


  • 2-s2.0-77958565426

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1536&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/530

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 6