Supervised feature extraction based on orthogonal discriminant projection

Authors:
Bo Li;Chao Wang;De-Shuang Huang
Affiliations:
School of Computer Science of Technology, Wuhan University of Science of Technology, Wuhan, Hubei 430081, China and Intelligent Computing Lab, Institute of Intelligent Machine, Chinese Academy of ...;Intelligent Computing Lab, Institute of Intelligent Machine, Chinese Academy of Science, P.O. Box 1130, Hefei, Anhui 230031, China;Intelligent Computing Lab, Institute of Intelligent Machine, Chinese Academy of Science, P.O. Box 1130, Hefei, Anhui 230031, China
Venue:
Neurocomputing
Year:
2009

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Abstract

In this paper, a supervised feature extraction method, named orthogonal discriminant projection (ODP), is presented. As an extension of spectral mapping method, the proposed algorithm maximizes the weighted difference between the non-local scatter and the local scatter. Moreover, the weights between two nodes of a graph are adjusted according to their class information and local information. Experiments on FERET face data, Yale face data and MNIST handwriting digits data validate that ODP can offer better recognition rate than some other feature extraction methods, such as local preserving projection (LPP), unsupervised discriminant projection (UDP) and orthogonal LPP (OLPP).