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Incremental Learning of Multivariate Gaussian Mixture Models

  • Paulo Martins Engel
  • Milton Roberto Heinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6404)

Abstract

This paper presents a new algorithm for unsupervised incremental learning based on a Bayesian framework. The algorithm, called IGMM (for Incremental Gaussian Mixture Model), creates and continually adjusts a Gaussian Mixture Model consistent to all sequentially presented data. IGMM is particularly useful for on-line incremental clustering of data streams, as encountered in the domain of mobile robotics and animats. It creates an incremental knowledge model of the domain consisting of primitive concepts involving all observed variables. We present some preliminary results obtained using synthetic data and also consider practical issues as convergence properties discuss future developments.

Keywords

Incremental Learning Unsupervised Learning Bayesian Methods Expectation-Maximization Algorithm Finite Mixtures Clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Paulo Martins Engel
    • 1
  • Milton Roberto Heinen
    • 1
  1. 1.UFRGS – Informatics InstitutePorto AlegreBrazil

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