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. © 2010 Springer-Verlag.
CITATION STYLE
Engel, P. M., & Heinen, M. R. (2010). Incremental learning of multivariate Gaussian mixture models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6404 LNAI, pp. 82–91). https://doi.org/10.1007/978-3-642-16138-4_9
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