Pseudo-inverse locality preserving projections

Rong Hua Li*, Zhiping Luo, Guoqiang Han

*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

This paper proposes a novel algorithm, named Pseudo-inverse Locality Preserving Projections (PLPP), for dimensionality reduction involving undersampled problems. This algorithm considers the matrix singularity caused by undersampled problems by substituting the Moore-Penrose pseudo-inverse for the inverse of the matrix. Under the pseudoinverse form eigenequation, the optimal locality preserving projections can be found by using the simultaneous diagonalization of three matrices technique, which intuitively solves the generalized eigenvalue decomposition problem. Theoretical analysis shows the flexible time complexity and better locality preserving power of PLPP. We compare the proposed PLPP with PCA, PCA+LDA, PCA+LPP on ORL face database and 20-Newsgroups text data sets. Experimental results show the effectiveness of the proposed algorithm.

Original languageEnglish
Title of host publicationCIS 2009 - 2009 International Conference on Computational Intelligence and Security
Pages363-367
Number of pages5
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 International Conference on Computational Intelligence and Security, CIS 2009 - Beijing, China
Duration: 11 Dec 200914 Dec 2009

Publication series

NameCIS 2009 - 2009 International Conference on Computational Intelligence and Security
Volume1

Conference

Conference2009 International Conference on Computational Intelligence and Security, CIS 2009
Country/TerritoryChina
CityBeijing
Period11/12/0914/12/09

Keywords

  • Dimensionality reduction
  • Locality preserving projections
  • Pseudo-inverse locality preserving projections

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