Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
532904 | Pattern Recognition | 2007 | 4 Pages |
Abstract
In this paper, a kernelized version of clustering-based discriminant analysis is proposed that we name KCDA. The main idea is to first map the original data into another high-dimensional space, and then to perform clustering-based discriminant analysis in the feature space. Kernel fuzzy c-means algorithm is used to do clustering for each class. A group of tests on two UCI standard benchmarks have been carried out that prove our proposed method is very promising.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Bo Ma, Hui-yang Qu, Hau-san Wong,