Kernel Pooled Local Subspaces for Classification
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.
MSU Digital Commons Citation
Zhang, Peng; Peng, Jing; and Domeniconi, Carlotta, "Kernel Pooled Local Subspaces for Classification" (2005). Department of Computer Science Faculty Scholarship and Creative Works. 360.