A Greedy EM Algorithm for Gaussian Mixture Learning |
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Authors: | Vlassis Nikos Likas Aristidis |
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Affiliation: | (1) RWCP Autonomous Learning Functions SNN, University of Amsterdam, Kruislaan 403, 1098 SJ cAmsterdam, The Netherlands;(2) Department of Computer Science, University of Ioannina, 45110 Ioannina, Greece |
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Abstract: | Learning a Gaussian mixture with a local algorithm like EM can be difficult because (i) the true number of mixing components
is usually unknown, (ii) there is no generally accepted method for parameter initialization, and (iii) the algorithm can get
trapped in one of the many local maxima of the likelihood function. In this paper we propose a greedy algorithm for learning
a Gaussian mixture which tries to overcome these limitations. In particular, starting with a single component and adding components
sequentially until a maximum number k, the algorithm is capable of achieving solutions superior to EM with k components in terms of the likelihood of a test set. The algorithm is based on recent theoretical results on incremental
mixture density estimation, and uses a combination of global and local search each time a new component is added to the mixture.
This revised version was published online in August 2006 with corrections to the Cover Date. |
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Keywords: | EM algorithm Gaussian mixture greedy learning |
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