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)

Citation Download Citation

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

ACCESS THE FULL ARTICLE

JOURNAL ARTICLE
3 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

CITATIONS

Cited by 10 scholarly publications.

Explore citations on Lens.org

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Feature extraction

Principal component analysis

Aerospace engineering

Databases

Detection and tracking algorithms

Statistical analysis

Computer vision technology

Show All Keywords

RELATED CONTENT

Graph optimized Laplacian eigenmaps for face recognition
Proceedings of SPIE (February 08 2015)

Manifold aware discriminant collaborative graph embedding for face recognition
Proceedings of SPIE (August 09 2018)

Direction analysis algorithm using statistical approaches
Proceedings of SPIE (June 02 2012)

Locally linear embedding based on local correlation
Proceedings of SPIE (January 13 2012)

A novel margin based linear embedding technique for visual object...
Proceedings of SPIE (January 23 2012)

A kernel density estimation based Bayesian classifier for celestial spectrum...
Proceedings of SPIE (October 30 2009)

Subscribe to Digital Library

Receive Erratum Email Alert

Show All Keywords

Keywords/Phrases

Search In:

Publication Years