Local-information-based uncorrelated feature extraction

Haitao Zhao; Shao-yuan Sun; Zhongliang Jing

doi:10.1117/1.2163873

1 February 2006 Local-information-based uncorrelated feature extraction

Haitao Zhao, Shao-yuan Sun, Zhongliang Jing

Author Affiliations +

Optical Engineering, Vol. 45, Issue 2, 020505 (February 2006). https://doi.org/10.1117/1.2163873

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.

©(2006) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Haitao Zhao, Shao-yuan Sun, and Zhongliang Jing "Local-information-based uncorrelated feature extraction," Optical Engineering 45(2), 020505 (1 February 2006). https://doi.org/10.1117/1.2163873

Published: 1 February 2006

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KEYWORDS

Feature extraction

Principal component analysis

Aerospace engineering

Databases

Detection and tracking algorithms

Statistical analysis

Computer vision technology

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