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Optical Engineering

Dimensionality reduction via locally reconstructive patch alignment
Author(s): Yi Chen; Zhong Jin; Jun Yin; Jie Zhu
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Paper Abstract

Based on the local patch concept, we proposed locally reconstructive patch alignment (LRPA) for dimensionality reduction. For each patch, LRPA aims to find the low-dimensional subspace in which the reconstruction error of the within-class nearest neighbors is minimized and the reconstruction error of the between-class nearest neighbors is maximized. LRPA preserves the local structure hidden in the high-dimensional space. More importantly, LRPA has natural connections with linear regression classification (LRC). While LRC uses reconstruction errors as the classification rule, a sample can be classified correctly when the within-class reconstruction error is minimal. The goal of LRPA makes it cooperate well with LRC. The experimental results on the extended Yale B (YALE-B), AR, PolyU finger knuckle print, and the palm print databases demonstrate LRPA plus LRC is an effective and robust pattern-recognition system.

Paper Details

Date Published: 3 August 2012
PDF: 11 pages
Opt. Eng. 51(7) 077208 doi: 10.1117/1.OE.51.7.077208
Published in: Optical Engineering Volume 51, Issue 7
Show Author Affiliations
Yi Chen, Nanjing Univ. of Science and Technology (China)
Zhong Jin, Nanjing Univ. of Science and Technology (China)
Jun Yin, Shanghai Maritime Univ. (China)
Jie Zhu, Nanjing Xiaozhuang Univ. (China)

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