Optimal Uncorrelated Unsupervised Discriminant Projection

Yu E Lin; Yong Cun Guo

doi:10.4028/www.scientific.net/AMM.701-702.54

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 701-702Optimal Uncorrelated Unsupervised Discriminant...

Optimal Uncorrelated Unsupervised Discriminant Projection

Abstract:

Unsupervised Discriminant Projection (UDP) is a typical manifold-based dimensionality reduction method, and has been successfully applied in face recognition. However, UDP suffers from the small sample size problem and usually deteriorates because the basis vectors of UDP are statistically correlated. In order to resolve these problems, we propose an Optimal Uncorrelated Unsupervised Discriminant Projection (OUUDP).The aim of OUUDP is to seek a feature submanifold such that the local scatter is minimized and non-local scatter scatter is maximized simultaneously in the embedding space by using a difference-based optimization objective function. Moreover, we impose an appropriate constraint to make the extracted features statistically uncorrelated. As a result, OUUDP can solve the small sample size problem and exploit statistically uncorrelated features. Experimental results on ORL databases demonstrate the effectiveness of the proposed algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 701-702)

Pages:

54-57

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.701-702.54

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Online since:

December 2014

Authors:

Yu E Lin, Yong Cun Guo

Keywords:

Dimensionality Reduction, Face Recognition, Manifold-Based, Small Sample Size Problem, Uncorrelated

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