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...

• Type: Journal
• Year: 2010
• Where: ICTAI
• Authors: Nicola Greggio, Alexandre Bernardino, Cecilia Laschi, Paolo Dario, José Santos-Victor
• Keywords: Artificial Intelligence, Expectation Maximization, Finite Mixture Model, ICTAI 2010, Modified Em Procedure