127 Scopus citations

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 L 2,1-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.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
Pages673-678
Number of pages6
Volume1
Publication statusPublished - 2010
Event24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10 - Atlanta, GA, United States
Duration: Jul 11 2010Jul 15 2010

Other

Other24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10
CountryUnited States
CityAtlanta, GA
Period7/11/107/15/10

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Zhao, Z., Wang, L., & Liu, H. (2010). Efficient spectral feature selection with minimum redundancy. In Proceedings of the National Conference on Artificial Intelligence (Vol. 1, pp. 673-678)