We investigate the use of subspace analysis methods for learning low-dimensional representations for classification. We propose a kernel-pooled local discriminant subspace method and compare it against competing techniques: kernel principal component analysis (KPCA) and generalized discriminant analysis (GDA) in classification problems. We evaluate the classification performance of the nearest-neighbor rule with each subspace representation. The experimental results using several data sets demonstrate the effectiveness and performance superiority of the kernel-pooled subspace method over competing methods such as KPCA and GDA in some classification problems.
|Number of pages||14|
|Journal||IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics|
|Publication status||Published - 1 Jun 2005|
- Kernel machines
- Nearest neighbors
- Subspace analysis