Yin, H. and Allinson, N. M.
(1995)
*On the distribution and convergence of feature space in self-organizing maps.*
Neural computation, 7
(6).
pp. 1178-1187.
ISSN 0899-7667

Full content URL: http://dx.doi.org/10.1162/neco.1995.7.6.1178

Full text not available from this repository.

Item Type: | Article |
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Item Status: | Live Archive |

## Abstract

In this paper an analysis of the statistical and the convergence properties of Kohonen's self-organizing map of any dimension is presented. Every feature in the map is considered as a sum of a number of random variables. We extend the Central Limit Theorem to a particular case, which is then applied to prove that the feature space during learning tends to multiple gaussian distributed stochastic processes, which will eventually converge in the mean-square sense to the probabilistic centers of input subsets to form a quantization mapping with a minimum mean squared distortion either globally or locally. The diminishing effect, as training progresses, of the initial states on the value of the feature map is also shown.

Keywords: | algorithm, article, artificial intelligence, artificial neural network, normal distribution, statistical model, statistics, Algorithms, Models, Statistical, Neural Networks (Computer), Stochastic Processes |
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Subjects: | G Mathematical and Computer Sciences > G400 Computer Science |

Divisions: | College of Science > School of Computer Science |

ID Code: | 8603 |

Deposited On: | 12 Jul 2013 10:19 |

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