Bayesian self-organising map for Gaussian mixtures

Yin, H. and Allinson, Nigel (2001) Bayesian self-organising map for Gaussian mixtures. IEE Proceedings: Vision, Image and Signal Processing, 148 (4). pp. 234-240. ISSN 1350-245X

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Item Type:Article
Item Status:Live Archive

Abstract

A Bayesian self-organising map (BSOM) is proposed for learning mixtures of Gaussian distributions. It is derived naturally from minimising the Kullback-Leibler divergence between the data density and the neural model. The inferred posterior probabilities of the neurons replace the common Euclidean distance winning rule and define explicitly the neighbourhood function. Learning can be retained in a small but fixed neighbourhood of the winner. The BSOM in turn provides an insight into the role of neighbourhood functions used in the common SOM. A formal comparison between the BSOM and the expectation-maximisation (EM) algorithm is also presented, together with experimental results.

Keywords:Learning algorithms, Learning systems, Mathematical models, Probability distributions, Self organizing maps, Bayesian self organising map, Expectation maximisation algorithm, Pattern recognition
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:8577
Deposited On:26 Apr 2013 10:52

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