Fast and Orthogonal Locality Preserving Projections for Dimensionality Reduction

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...

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Veröffentlicht in:IEEE transactions on image processing Jg. 26; H. 10; S. 5019 - 5030
Hauptverfasser: Wang, Rong, Nie, Feiping, Hong, Richang, Chang, Xiaojun, Yang, Xiaojun, Yu, Weizhong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.10.2017
Schlagworte:
Algorithm design and analysis
Dimensionality reduction (DR)
Eigenvalues and eigenfunctions
Face recognition
hyperspectral image (HSI) classification
Laplace equations
locality preserving projections (LPP)
Manifolds
Optimization
Training data
ISSN:1057-7149, 1941-0042, 1941-0042
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Zusammenfassung: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.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2017.2726188