Orthogonal locality minimizing globality maximizing projections for feature extraction

Feiping Nie; Shiming Xiang; Yangqiu Song; Changshui Zhang

doi:10.1117/1.3067869

1 January 2009 Orthogonal locality minimizing globality maximizing projections for feature extraction

Feiping Nie, Shiming Xiang, Yangqiu Song, Changshui Zhang

Author Affiliations +

Optical Engineering, Vol. 48, Issue 1, 017202 (January 2009). https://doi.org/10.1117/1.3067869

Abstract

Locality preserving projections (LPP) is a recently developed linear-feature extraction algorithm that has been frequently used in the task of face recognition and other applications. However, LPP does not satisfy the shift-invariance property, which should be satisfied by a linear-feature extraction algorithm. In this paper, we analyze the reason and derive the shift-invariant LPP algorithm. Based on the analysis of the geometrical meaning of the shift-invariant LPP algorithm, we propose two algorithms to minimize the locality and maximize the globality under an orthogonal projection matrix. Experimental results on face recognition are presented to demonstrate the effectiveness of the proposed algorithms.

©(2009) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Feiping Nie, Shiming Xiang, Yangqiu Song, and Changshui Zhang "Orthogonal locality minimizing globality maximizing projections for feature extraction," Optical Engineering 48(1), 017202 (1 January 2009). https://doi.org/10.1117/1.3067869

Published: 1 January 2009

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