Optimal feature selection for robust classification via l2,1-norms regularization

Jiajun Wen, Zhihui Lai, Wai Keung Wong, Jinrong Cui, Minghua Wan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

16 Citations (Scopus)

Abstract

This paper aims to explore the optimal feature selection with dimensionality reduction and jointly sparse representation scheme for classification. The proposed method is called Optimal Feature Selection Classification (OFSC). Our model simultaneously learns an orthogonal subspace for jointly sparse feature selection and representation via l2,1-norms regularization. To solve the proposed model, an alternately iterative algorithm is proposed to optimize both the jointly sparse projection matrix and representation matrix. Experimental results on three public face datasets and one action dataset validate the quick convergence of our algorithm and show that the proposed method is more competitive than the state-of-the-art methods.
Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherIEEE
Pages517-521
Number of pages5
ISBN (Electronic)9781479952083
DOIs
Publication statusPublished - 1 Jan 2014
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014

Conference

Conference22nd International Conference on Pattern Recognition, ICPR 2014
CountrySweden
CityStockholm
Period24/08/1428/08/14

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this