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.

An unmanned marine vehicle thruster fault diagnosis scheme based on OFNDA

“…As such, it is obvious that the main goal of feature dimensionality reduction is to reduce the number of features without compromising the quality of classification. Generally, dimension reduction approaches can be classified into linear and nonlinear methods (Lin & Guo 2015). The choice of linear and nonlinear techniques will be determined by the nature of the classification problem.…”