A New Self-Organizing Map based on Kernel Concepts


The KIPS Transactions:PartB , Vol. 13, No. 4, pp. 439-448, Aug. 2006
10.3745/KIPSTB.2006.13.4.439,   PDF Download:

Abstract

Previous recognition/clustering algorithms such as Kohonen SOM(Self-Organizing Map), MLP(Multi-Layer Perceptron) and SVM(Support Vector Machine) might not adapt to unexpected input pattern. And it's recognition rate depends highly on the complexity of own training patterns. We could make up for and improve the weak points with lowering complexity of original problem without losing original characteristics. There are so many ways to lower complexity of the problem, and we chose a kernel concepts as an approach to do it. In this paper, using a kernel concepts, original data are mapped to hyper-dimension space which is near infinite dimension. Therefore, transferred data into the hyper-dimension are distributed spasely rather than originally distributed so as to guarantee the rate to be risen. Estimating ratio of recognition is based on a new similarity-probing and learning method that are proposed in this paper. Using CEDAR DB which data is written in cursive letters, 0 to 9, we compare a recognition/clustering performance of kSOM that is proposed in this paper with previous SOM.


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Cite this article
[IEEE Style]
S. M. Cheong, K. B. Kim, S. J. Hong, "A New Self-Organizing Map based on Kernel Concepts," The KIPS Transactions:PartB , vol. 13, no. 4, pp. 439-448, 2006. DOI: 10.3745/KIPSTB.2006.13.4.439.

[ACM Style]
Sung Moon Cheong, Ki Bom Kim, and Soon Jwa Hong. 2006. A New Self-Organizing Map based on Kernel Concepts. The KIPS Transactions:PartB , 13, 4, (2006), 439-448. DOI: 10.3745/KIPSTB.2006.13.4.439.