Literature DB >> 10578045

Comparison of SOM point densities based on different criteria.

T Kohonen1.   

Abstract

Point densities of model (codebook) vectors in self-organizing maps (SOMs) are evaluated in this article. For a few one-dimensional SOMs with finite grid lengths and a given probability density function of the input, the numerically exact point densities have been computed. The point density derived from the SOM algorithm turned out to be different from that minimizing the SOM distortion measure, showing that the model vectors produced by the basic SOM algorithm in general do not exactly coincide with the optimum of the distortion measure. A new computing technique based on the calculus of variations has been introduced. It was applied to the computation of point densities derived from the distortion measure for both the classical vector quantization and the SOM with general but equal dimensionality of the input vectors and the grid, respectively. The power laws in the continuum limit obtained in these cases were found to be identical.

Mesh:

Year:  1999        PMID: 10578045     DOI: 10.1162/089976699300016098

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  2 in total

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Authors:  Mario Huerta; Juan Cedano; Dario Peña; Antonio Rodriguez; Enrique Querol
Journal:  BMC Bioinformatics       Date:  2009-05-09       Impact factor: 3.169

2.  Round Randomized Learning Vector Quantization for Brain Tumor Imaging.

Authors:  Siti Norul Huda Sheikh Abdullah; Farah Aqilah Bohani; Baher H Nayef; Shahnorbanun Sahran; Omar Al Akash; Rizuana Iqbal Hussain; Fuad Ismail
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  2 in total

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