Modeling High-Dimensional Probability Distributions via Linear Manifold Clusters
Rave Harpaz, Robert M. Haralick · 2007
One of the ultimate goals of cluster analysis is not only to reveal structure but also to understand it. Most clustering methods focus only on the grouping aspect and do not provide a descriptive model with which the population underlying the data can be described or with which statistical inference such as predictions can be made. Linear manifold clustering seeks to identify groups of points that lie on lower dimensional linear manifolds. In this paper we present a non-parametric density estimation modeling technique by which data that lies in a mixture of linear manifolds can be described and with which statistical inference can based on. The efficacy of this technique is demonstrated by a target recognition experiment, where image pixels represented by high-dimensional feature vectors are classified with an error rate close to 0.1 using a probabilistic classifier constructed from mixture models of linear manifolds.