Kernel Pooled Local Subspaces for Classification

Peng Zhang, Jing Peng, Carlotta Domeniconi
2003 2003 Conference on Computer Vision and Pattern Recognition Workshop  
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
more » ... strate the effectiveness and performance superiority of the kernel-pooled subspace method over competing methods such as KPCA and GDA in some classification problems.
doi:10.1109/cvprw.2003.10060 dblp:conf/cvpr/ZhangPD03 fatcat:3osmta7jarcipfilovuh3f7aam