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Authors: Nicola Greggio 1 ; Alexandre Bernardino 2 and José Santos-Victor 2

Affiliations: 1 ARTS Lab - Scuola Superiore S. Anna;Instituto Superior Técnico, Portugal ; 2 Instituto Superior Técnico, Portugal

Keyword(s): Unsupervised learning, Self-adapting gaussian mixture, Expectation maximization, Machine learning, Clustering.

Related Ontology Subjects/Areas/Topics: Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Machine Learning in Control Applications ; Optimization Algorithms

Abstract: Split-and-merge techniques have been demonstrated to be effective in overtaking the convergence problems in classical EM. In this paper we follow a split-and-merge approach and we propose a new EM algorithm that makes use of a on-line variable number of mixture Gaussians components. We introduce a measure of the similarities to decide when to merge components. A set of adaptive thresholds keeps the number of mixture components close to optimal values. For sake of computational burden, our algorithm starts with a low initial number of Gaussians, adjusting it in runtime, if necessary. We show the effectivity of the method in a series of simulated experiments. Additionally, we illustrate the convergence rates of of the proposed algorithms with respect to the classical EM.

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Paper citation in several formats:
Greggio, N.; Bernardino, A. and Santos-Victor, J. (2010). A PRACTICAL METHOD FOR SELF-ADAPTING GAUSSIAN EXPECTATION MAXIMIZATION. In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8425-00-3; ISSN 2184-2809, pages 36-44. DOI: 10.5220/0002894600360044

@conference{icinco10,
author={Nicola Greggio. and Alexandre Bernardino. and José Santos{-}Victor.},
title={A PRACTICAL METHOD FOR SELF-ADAPTING GAUSSIAN EXPECTATION MAXIMIZATION},
booktitle={Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2010},
pages={36-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002894600360044},
isbn={978-989-8425-00-3},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - A PRACTICAL METHOD FOR SELF-ADAPTING GAUSSIAN EXPECTATION MAXIMIZATION
SN - 978-989-8425-00-3
IS - 2184-2809
AU - Greggio, N.
AU - Bernardino, A.
AU - Santos-Victor, J.
PY - 2010
SP - 36
EP - 44
DO - 10.5220/0002894600360044