Multiresolution training of Kohonen neural networks

Dan E. Tamir

doi:10.1117/12.735394

17 September 2007 Multiresolution training of Kohonen neural networks

Dan E. Tamir

Proceedings Volume 6700, Mathematics of Data/Image Pattern Recognition, Compression, Coding, and Encryption X, with Applications; 67000B (2007) https://doi.org/10.1117/12.735394
Event: Optical Engineering + Applications, 2007, San Diego, California, United States

Abstract

This paper analyses a trade-off between convergence rate and distortion obtained through a multi-resolution training of a Kohonen Competitive Neural Network. Empirical results show that a multi-resolution approach can improve the training stage of several unsupervised pattern classification algorithms including K-means clustering, LBG vector quantization, and competitive neural networks. While, previous research concentrated on convergence rate of on-line unsupervised training. New results, reported in this paper, show that the multi-resolution approach can be used to improve training quality (measured as a derivative of the rate distortion function) on the account of convergence speed. The probability of achieving a desired point in the quality/convergence-rate space of Kohonen Competitive Neural Networks (KCNN) is evaluated using a detailed Monte Carlo set of experiments. It is shown that multi-resolution can reduce the distortion by a factor of 1.5 to 6 while maintaining the convergence rate of traditional KCNN. Alternatively, the convergence rate can be improved without loss of quality. The experiments include a controlled set of synthetic data, as well as, image data. Experimental results are reported and evaluated.

Citation Download Citation

Dan E. Tamir "Multiresolution training of Kohonen neural networks", Proc. SPIE 6700, Mathematics of Data/Image Pattern Recognition, Compression, Coding, and Encryption X, with Applications, 67000B (17 September 2007); https://doi.org/10.1117/12.735394

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KEYWORDS

Distortion

Quantization

Monte Carlo methods

Neural networks

Quality measurement

RGB color model

Image compression

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