Sampling Methods for Unsupervised Learning

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Rob Fergus, Andrew Zisserman, Pietro Perona


We present an algorithm to overcome the local maxima problem in es- timating the parameters of mixture models. It combines existing ap- proaches from both EM and a robust fitting algorithm, RANSAC, to give a data-driven stochastic learning scheme. Minimal subsets of data points, sufficient to constrain the parameters of the model, are drawn from pro- posal densities to discover new regions of high likelihood. The proposal densities are learnt using EM and bias the sampling toward promising solutions. The algorithm is computationally efficient, as well as effective at escaping from local maxima. We compare it with alternative methods, including EM and RANSAC, on both challenging synthetic data and the computer vision problem of alpha-matting.