Stochastic analysis and comparison of Kohonen SOM with optimal filter

Yin, H. and Allinson, Nigel (1993) Stochastic analysis and comparison of Kohonen SOM with optimal filter. In: Third International Conference on Artificial Neural Networks, 1993, 25-27 May 1993, Brighton.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


In this paper a detailed investigation of the statistical and convergent properties of Kohonen's Self-Organising Map (SOM) algorithm is presented. The Central Limit Theorem has been extended and then applied to prove that the feature space in SOM learning is an approximation to Gaussian distributed stochastic processes, and will eventually converge in the mean-square sense to the density centres of the input probabilistic sub-domains. We demonstrate that by combining the SOM with a Kalman filter will smooth and accelerate the learning and convergence of the SOM, especially in early training stages. We also present a discussion on the local optimization of the SOM algorithm.

Keywords:Algorithms, Analysis, Kalman filtering, Mathematical techniques, Central limit theorem, Kohonen self organizing map, Stochastic analysis, Neural networks
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G730 Neural Computing
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:8614
Deposited On:31 May 2013 11:34

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