Statistics and Data Science Seminar
Jie Yang
UIC
Classification Based on Permanent Process with Cyclic Approximation
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.
Wednesday February 23, 2011 at 3:00 PM in SEO 636