Unsupervised Greedy Learning of Finite Mixture Models

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Unsupervised Greedy Learning of Finite Mixture Models
This work deals with a new technique for the estimation of the parameters and number of components in a finite mixture model. The learning procedure is performed by means of a expectation maximization (EM) methodology. The key feature of our approach is related to a top-down hierarchical search for the number of components, together with the integration of the model selection criterion within a modified EM procedure, used for the learning the mixture parameters. We start with a single component covering the whole data set. Then new components are added and optimized to best cover the data. The process is recursive and builds a binary tree like structure that effectively explores the search space. We show that our approach is faster that state-of-theart alternatives, is insensitive to initialization, and has better data fits in average. We elucidate this through a series of experiments, both with synthetic and real data. Keywords-Machine Learning, Unsupervised Clustering, SelfAdapting E...
Nicola Greggio, Alexandre Bernardino, Cecilia Lasc
Added 04 Mar 2011
Updated 04 Mar 2011
Type Journal
Year 2010
Authors Nicola Greggio, Alexandre Bernardino, Cecilia Laschi, Paolo Dario, José Santos-Victor
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