Discriminative sparsity preserving graph embedding

Jianping Gou, Lan Du, Keyang Cheng, Yingfeng Cai

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

6 Citations (Scopus)

Abstract

In this paper, we propose a new dimensionality reduction method called discriminative sparsity preserving graph embedding (DSPGE). Unlike many existing graph embedding methods such as locality preserving projections (LPP) and sparsity preserving projections (SPP), the aim of DSPGE is to preserve the sparse reconstructive relationships of data while simultaneously capture the geometric and discriminant structure of data in the embedding space. Through the sparse reconstruction and class-specific adjacent graphs, DSPGE characterizes the intra-class and inter-class sparsity preserving scatters, seeking to achieve the optimal projections that simultaneously maximize the inter-class sparsity preserving scatter and minimize intra-class sparsity preserving scatter. The effectiveness of the proposed DSPGE is demonstrated on two popular face databases, compared to up-to-date methods. The experimental results show that DSPGE outperforms the competing methods with the satisfactory classification performance.

Original languageEnglish
Title of host publicationCEC 2016
Subtitle of host publication2016 IEEE Congress on Evolutionary Computation
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4250-4257
Number of pages8
ISBN (Electronic)9781509006229, 9781509006243
ISBN (Print)9781509006236
DOIs
Publication statusPublished - 14 Nov 2016
Externally publishedYes
Event2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Other

Other2016 IEEE Congress on Evolutionary Computation, CEC 2016
CountryCanada
CityVancouver
Period24/07/1629/07/16

Keywords

  • dimensionality reduction
  • graph embedding
  • sparse representation,
  • face recognition

Fingerprint Dive into the research topics of 'Discriminative sparsity preserving graph embedding'. Together they form a unique fingerprint.

Cite this