Classification trought density estimation

TURNER C.

New Jersey Institue of Technology, New Jersey-USA

Congreso; Frontiers of Computational and Biological Methods; 2009

A unified variational methodology is developed for classification and clustering problems, and tested in the classification of tumors from gene expression data. It is based on fluid-like flows in feature space that clus- ter a set of observations by transforming them into likely samples from p isotropic Gaussians, where p is the number of classes sought. The methodology blurs the distinction between training and testing popula- tions through the soft assignment of both to classes. The observations act as Lagrangian markers for the flows, comparatively active or passive depending on the current strength of the assignment to the corresponding class.