Spectral feature selection for supervised and unsupervised learning

Zheng Zhao, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

473 Scopus citations

Abstract

Feature selection aims to reduce dimensionality for building comprehensible learning models with good generalization performance. Feature selection algorithms are largely studied separately according to the type of learning: supervised or unsupervised. This work exploits intrinsic properties underlying supervised and unsupervised feature selection algorithms, and proposes a unified framework for feature selection based on spectral graph theory. The proposed framework is able to generate families of algorithms for both supervised and unsupervised feature selection. And we show that existing powerful algorithms such as ReliefF (supervised) and Laplacian Score (unsupervised) are special cases of the proposed framework. To the best of our knowledge, this work is the first attempt to unify supervised and unsupervised feature selection, and enable their joint study under a general framework. Experiments demonstrated the efficacy of the novel algorithms derived from the framework.

Original languageEnglish (US)
Title of host publicationACM International Conference Proceeding Series
Pages1151-1157
Number of pages7
Volume227
DOIs
StatePublished - 2007
Event24th International Conference on Machine Learning, ICML 2007 - Corvalis, OR, United States
Duration: Jun 20 2007Jun 24 2007

Other

Other24th International Conference on Machine Learning, ICML 2007
CountryUnited States
CityCorvalis, OR
Period6/20/076/24/07

ASJC Scopus subject areas

  • Human-Computer Interaction

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  • Cite this

    Zhao, Z., & Liu, H. (2007). Spectral feature selection for supervised and unsupervised learning. In ACM International Conference Proceeding Series (Vol. 227, pp. 1151-1157) https://doi.org/10.1145/1273496.1273641