Sparse representation based spectral regression

Guo Xian Yu, Zhi Wen Yu, Jing Hua, Xuan Li, Jia You

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

3 Citations (Scopus)

Abstract

Spectral regression is a newly proposed method for dimensionality reduction, which is also based on graph embedding but less time and space consuming. However, like many methods based on neighborhood graphs, it still focuses on local manifold smoothness and ignores discriminative information among samples. In this paper, instead of making use of a neighborhood graph, we take advantage of a global graph defined by the coefficients of sparse representation and propose a method called Sparse Representation based Spectral Regression (SpSR) on this graph. This graph is data-driven, discriminative and robust to noise features. Experimental results on facial images feature extraction tasks demonstrate these advantages.
Original languageEnglish
Title of host publicationProceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Pages532-537
Number of pages6
Volume2
DOIs
Publication statusPublished - 7 Nov 2011
Event2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China
Duration: 10 Jul 201113 Jul 2011

Conference

Conference2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
CountryChina
CityGuilin, Guangxi
Period10/07/1113/07/11

Keywords

  • Discriminative
  • Neighborhood graph
  • Sparse representation
  • Spectral regression

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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