Joint feature selection and subspace learning

Quanquan Gu, Zhenhui Li, Jiawei Han

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

143 Scopus citations

Abstract

Dimensionality reduction is a very important topic in machine learning. It can be generally classified into two categories: feature selection and subspace learning. In the past decades, many methods have been proposed for dimensionality reduction. However, most of these works study feature selection and subspace learning independently. In this paper, we present a framework for joint feature selection and subspace learning. We reformulate the subspace learning problem and use L2,1-norm on the projection matrix to achieve row-sparsity, which leads to selecting relevant features and learning transformation simultaneously. We discuss two situations of the proposed framework, and present their optimization algorithms. Experiments on benchmark face recognition data sets illustrate that the proposed framework outperforms the state of the art methods overwhelmingly.

Original languageEnglish (US)
Title of host publicationIJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
Pages1294-1299
Number of pages6
DOIs
StatePublished - Dec 1 2011
Event22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia, Spain
Duration: Jul 16 2011Jul 22 2011

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Other

Other22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
CountrySpain
CityBarcelona, Catalonia
Period7/16/117/22/11

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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  • Cite this

    Gu, Q., Li, Z., & Han, J. (2011). Joint feature selection and subspace learning. In IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence (pp. 1294-1299). (IJCAI International Joint Conference on Artificial Intelligence). https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-219