Model probability in self-organising maps

Authors:
Anastassia Angelopoulou;Alexandra Psarrou;José García-Rodríguez;Markos Mentzelopoulos;Gaurav Gupta
Affiliations:
Department of Computer Science and Software Engineering, University of Westminster, UK;Department of Computer Science and Software Engineering, University of Westminster, UK;Department of Computing Technology, University of Alicante, Spain;Department of Computer Science and Software Engineering, University of Westminster, UK;Department of Computer Science and Software Engineering, University of Westminster, UK
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Year:
2013

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Abstract

Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced unsupervised self-organising network for the modelling of visual objects. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product.