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Optical Engineering • Open Access

Local-information-based uncorrelated feature extraction
Author(s): Haitao Zhao; Shao-yuan Sun; Zhongliang Jing

Paper Abstract

In the past few years, the computer vision and pattern recognition community has witnessed the rapid growth of a new kind of feature extraction method, the manifold learning methods, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among them, locality preserving projection (LPP) is one of the most promising feature extraction techniques. Based on LPP, we propose a novel feature extraction method, called uncorrelated locality preserving projection (ULPP). We show that the extracted features via ULPP are statistically uncorrelated, which is desirable for many pattern analysis applications. We compare the proposed ULPP approach with LPP and principal component analysis (PCA) on the publicly available data sets, FERET and AR. Experimental results suggest that the proposed ULPP approach provides a better representation of the data and achieves much higher recognition accuracies.

Paper Details

Date Published: 1 February 2006
PDF: 3 pages
Opt. Eng. 45(2) 020505 doi: 10.1117/1.2163873
Published in: Optical Engineering Volume 45, Issue 2
Show Author Affiliations
Haitao Zhao, Institute of Aerospace Science & Technology (China)
Shao-yuan Sun, Donghua Univ. (China)
Zhongliang Jing, Shanghai Jiaotong Univ. (China)

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