Fast and orthogonal locality preserving projections for dimensionality reduction

Rong Wang, Feiping Nie, Richang Hong, Xiaojun Chang, Xiaojun Yang, Weizhong Yu

Research output: Contribution to journalArticleResearchpeer-review

98 Citations (Scopus)

Abstract

The locality preserving projections (LPP) algorithm is a recently developed linear dimensionality reduction algorithm that has been frequently used in face recognition and other applications. However, the projection matrix in LPP is not orthogonal, thus creating difficulties for both reconstruction and other applications. As the orthogonality property is desirable, orthogonal LPP (OLPP) has been proposed so that an orthogonal projection matrix can be obtained based on a step by step procedure; however, this makes the algorithm computationally more expensive. Therefore, in this paper, we propose a fast and orthogonal version of LPP, called FOLPP, which simultaneously minimizes the locality and maximizes the globality under the orthogonal constraint. As a result, the computation burden of the proposed algorithm can be effectively alleviated compared with the OLPP algorithm. Experimental results on two face recognition data sets and two hyperspectral data sets are presented to demonstrate the effectiveness of the proposed algorithm.

Original languageEnglish
Article number7976386
Pages (from-to)5019-5030
Number of pages12
JournalIEEE Transactions on Image Processing
Volume26
Issue number10
DOIs
Publication statusPublished - Oct 2017
Externally publishedYes

Keywords

  • Dimensionality reduction (DR)
  • face recognition
  • hyperspectral image (HSI) classification
  • locality preserving projections (LPP)

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