Jie Yang : Classification Based on Permanent Process with Cyclic Approximation

Posted by , part of the Statistics and Data Science Seminar.

At

Feb. 23, 2011, 3 p.m.

In

SEO 636

Abstract

We propose a stochastic classification model based on a permanent process. Unlike many research works in the literature, the proposed model assumes only exchangeability instead of independence on observations. Regardless of the number of classes or the dimension of the feature variables, the model may require only 2-3 parameters for fitting the covariance structure within clusters. It works well even if the class occupies non-convex, disjoint regions, or regions overlapped with other classes in the feature space. The proposed model requires calculation of ratios of weighted permanents, which is an NP-hard problem. We propose a series of approximations for weighted permanent ratio based on cyclic expansions. The classification based on cyclic approximations works reasonably well.