Model probability in self-organising maps

Angelopoulou, A, Psarrou, A, García-Rodríguez, J, Mentzelopoulos, M and Gupta, G (2013) Model probability in self-organising maps. In: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, 12 Jun 2013, Puerto de la Cruz, Tenerife, Spain.

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Official URL: https://dx.doi.org/10.1007/978-3-642-38682-4_1

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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Minimum Description Length, Self-organising networks, Shape Modelling
Subjects: University of Westminster > Science and Technology
SWORD Depositor: repository@westminster.ac.uk
Depositing User: repository@westminster.ac.uk
Date Deposited: 18 Nov 2015 15:37
Last Modified: 06 Jan 2016 16:44
URI: http://westminsterresearch.wmin.ac.uk/id/eprint/16025

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