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Date: Wednesday February the 28th, 2007
Start Time: 2:15pm, Zepler, Seminar Room 2 Speakers: Roland Wilson, University of Warwick Gaussian mixtures have been in use for many years because they offer a general way of approximating arbitrary probability densities, in particular those with long tails or multiple modes. While this makes them attractive, they are beset by difficulties of identification and computation: how many components are required and how might they be estimated from a given data set? A variety of techniques have been proposed, ranging in complexity from EM algorithms to Reversible Jump MCMC. The approach I am proposing takes a different line of attack, using a recursive greedy algorithm to approximate the density to a given level of accuracy, which can be specified by the user. A simple MCMC algorithm is used at each step of the process to determine whether a given subset of the data should be split, in a Bayesian formalism. The technique has been used in a variety of applications in computer vision, from segmentation to motion modelling and manifold learning. It has been extended to networks of models, in a fashion which shares some features with self-organising maps. In the talk, I shall give an outline of the key features of MGM and discuss the issue of model selection, as well as presenting preliminary results on real data. |
