On the equivalence between kernel self-organising maps and self-organising mixture density networks
✍ Scribed by Hujun Yin
- Publisher
- Elsevier Science
- Year
- 2006
- Tongue
- English
- Weight
- 214 KB
- Volume
- 19
- Category
- Article
- ISSN
- 0893-6080
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✦ Synopsis
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.