Kernel clustering-based discriminant analysis
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 324-327 |
| Journal / Publication | Pattern Recognition |
| Volume | 40 |
| Issue number | 1 |
| Publication status | Published - Jan 2007 |
Link(s)
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. © 2006 Pattern Recognition Society.
Research Area(s)
- Clustering-based discriminant analysis (CDA), Kernel clustering-based discriminant analysis (KCDA), Kernel fuzzy c-means, Kernel linear discriminant analysis (KLDA), Linear discriminant analysis (LDA)
Citation Format(s)
Kernel clustering-based discriminant analysis. / Ma, Bo; Qu, Hui-yang; Wong, Hau-san.
In: Pattern Recognition, Vol. 40, No. 1, 01.2007, p. 324-327.
In: Pattern Recognition, Vol. 40, No. 1, 01.2007, p. 324-327.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
