Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
408809 | Neurocomputing | 2009 | 6 Pages |
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).
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Bo Li, Chao Wang, De-Shuang Huang,