Fergus, Rob and Zisserman, Andrew and Perona, Pietro (2005) Sampling Methods for Unsupervised Learning. In: Advances in Neural Information Processing Systems 17 (NIPS 2004). Advances in Neural Information Processing Systems. No.17. MIT Press , Cambridge, MA, pp. 433-440. ISBN 0-262-19534-8. https://resolver.caltech.edu/CaltechAUTHORS:20160314-151925758
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Abstract
We present an algorithm to overcome the local maxima problem in estimating the parameters of mixture models. It combines existing approaches 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 proposal 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.
Item Type: | Book Section | ||||||||||
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Additional Information: | © 2005 Massachusetts Institute of Technology. Funding was provided by EC Project CogViSys, EC NOE Pascal, Caltech CNSE, the NSF and the UK EPSRC. Thanks to F. Schaffalitzky & P. Torr for useful discussions. | ||||||||||
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Series Name: | Advances in Neural Information Processing Systems | ||||||||||
Issue or Number: | 17 | ||||||||||
Record Number: | CaltechAUTHORS:20160314-151925758 | ||||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20160314-151925758 | ||||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||
ID Code: | 65340 | ||||||||||
Collection: | CaltechAUTHORS | ||||||||||
Deposited By: | Kristin Buxton | ||||||||||
Deposited On: | 14 Mar 2016 23:52 | ||||||||||
Last Modified: | 03 Oct 2019 09:46 |
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