Mixture of experts classification using a hierarchical mixture model

Authors:
Michalis K. Titsias;Aristidis Likas
Affiliations:
Department of Computer Science, University of Ioannina, 45110 Ioannina, Greece;Department of Computer Science, University of Ioannina, 45110 Ioannina, Greece
Venue:
Neural Computation
Year:
2002

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Abstract

A three-level hierarchical mixture model for classification is presented that models the following data generation process: (1) the data are generated by a finite number of sources (clusters), and (2) the generation mechanism of each source assumes the existence of individual internal class-labeled sources (subclusters of the external cluster). The model estimates the posterior probability of class membership similar to a mixture of experts classifier. In order to learn the parameters of the model, we have developed a general training approach based on maximum likelihood that results in two efficient training algorithms. Compared to other classification mixture models, the proposed hierarchical model exhibits several advantages and provides improved classification performance as indicated by the experimental results.