Cluster based nonlinear principle component analysis

Abstract

In the field of computer vision, principle component analysis (PCA) is often used to provide statistical models of shape, deformation or appearance. This simple statistical model provides a constrained, compact approach to model based vision. However, as larger problems are considered, high dimensionality and nonlinearity make linear PCA an unsuitable and unreliable approach. A nonlinear PCA (NLPCA) technique is proposed which uses cluster analysis and dimensional reduction to provide a fast, robust solution. Simulation results on both 2D contour models and greyscale images are presented.