A new solution scheme of unsupervised locality preserving projection method for the SSS problem

Yong Xu, Dapeng Zhang

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

2 Citations (Scopus)

Abstract

When locality preserving projection (LPP) method was originally proposed, it takes as the LPP solution the minimum eigenvalue solution of an eigenequation. After that, LPP has been used for image recognition problems such as face recognition. However, almost no researcher realizes that LPP usually encounters several difficulties when applied to the image recognition problem. For example, since image recognition problems are usually small sample size (SSS) problems, the corresponding eigenequation cannot be directly solved. In addition, it seems that even if one can obtain the solution of the eigenequation by using the numerical analysis approach, the obtained conventional LPP solution might produce the 'presentation confusion' problem for samples from different classes, which is disadvantageous for the classification to procedure a high accuracy. In this paper we first thoroughly investigate the characteristics and drawbacks of the conventional LPP solution in the small sample size (SSS) problem in which the sample number is smaller than the data dimension. In order to overcome these drawbacks, we propose a new LPP solution for the SSS problem, which has clear physical meaning and can be directly and easily worked out because it is generated from a non-singular eigenequation. Experimental results the proposed solution scheme can produce a much lower classification error rate than the conventional LPP solution.
Original languageEnglish
Title of host publicationStructural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, SSPR and SPR 2008, Proceedings
Pages775-781
Number of pages7
DOIs
Publication statusPublished - 1 Dec 2008
EventJoint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, SSPR and SPR 2008 - Orlando, FL, United States
Duration: 4 Dec 20086 Dec 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5342 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceJoint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, SSPR and SPR 2008
Country/TerritoryUnited States
CityOrlando, FL
Period4/12/086/12/08

Keywords

  • Face recognition
  • Feature extraction
  • Locality preserving projection

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this