Discriminant Analysis with Local Gaussian Similarity Preserving for Feature Extraction

摘要

In this paper, we propose a novel discriminant analysis with local Gaussian similarity preserving (DA-LGSP) method for feature extraction. DA-LGSP can be viewed as a linear approximation of manifold learning based approach which seeks to find a linear projection that maximizes the between-class dissimilarities under the constraint of locality preserving. The local geometry of each point is preserved by the Gaussian coefficients of its neighbors, meanwhile the between-class dissimilarities are represented by Euclidean distances. Experiments are conducted on USPA data, COIL-20 dataset, ORL dataset and FERET dataset. The performance of the proposed method demonstrates that DA-LGSP is effective in feature extraction.