Cluster based nonlinear principle component analysis
Richard Bowden, T. A. Mitchell, Mansoor Sarhadi · Electronics Letters · 1997
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.