Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
530980 | Pattern Recognition | 2010 | 11 Pages |
Abstract
Despite the general success in the pattern recognition community, linear discriminant analysis (LDA) has four intrinsic drawbacks. In this paper, we propose a new feature extraction algorithm, namely, local sampling mean discriminant analysis (LSMDA), to make up for the first three drawbacks, and a generalized re-weighting (GRW) framework to make up for the fourth drawback. Extensive experiments are conducted on both synthetic and real-world datasets to evaluate the classification performance of our work. The experimental results demonstrate the effectiveness of both LSMDA and the GRW framework in classifications.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Jing Chai, Hongwei Liu, Zheng Bao,