Machine Learning
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Neural Computation
Self-Organizing Maps
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IEEE Transactions on Neural Networks
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Neural gas clustering for dissimilarity data with continuous prototypes
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Topographic mapping of large dissimilarity data sets
Neural Computation
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KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
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IEEE Transactions on Neural Networks
Relational generative topographic mapping
Neurocomputing
Topographic mapping of dissimilarity data
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Linear time heuristics for topographic mapping of dissimilarity data
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Clustering very large dissimilarity data sets
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
Approximation techniques for clustering dissimilarity data
Neurocomputing
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ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
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The kernel method has become a useful trick and has been widely applied to various learning models to extend their nonlinear approximation and classification capabilities. Such extensions have also recently occurred to the Self-Organising Map (SOM). In this paper, two recently proposed kernel SOMs are reviewed, together with their link to an energy function. The Self-Organising Mixture Network is an extension of the SOM for mixture density modelling. This paper shows that with an isotropic, density-type kernel function, the kernel SOM is equivalent to a homoscedastic Self-Organising Mixture Network, an entropy-based density estimator. This revelation on the one hand explains that kernelising SOM can improve classification performance by acquiring better probability models of the data; but on the other hand it also explains that the SOM already naturally approximates the kernel method.