Abstract
Data compression of high dimentional complex patterns is one of the most challaging task of information technology today. Proposed approach is based on feature extraction procedure which maps original patterns into features (factors) space of reduced, possibily very small, dimension. In this paper, we outline that Hebbian unsupervised learning of Hopfield-like neural network is a natural procedure for factor extraction. Due to this learning, factors become the attractors of network dynamics, hence they can be revealed by the random search. The neurodynamics is modeled by Single-Step approximation which is known [5] to be rather accurate for sparsely encoded Hopfield-network. This paper is limited to the case of sparsely encoded factors, but it is not realy constraint for data compression.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Amari, S., Maginu, K. (1988). Statistical neurodynamics of associative memory. Neural Networks, 1, 63–73.
Amit, D. J., Gutfreund, H., Sompolinsky, H. (1987). Statistical mechanics of neural networks near saturation. Annal of Physics, 173, 30–67.
Bucingham, J., Willshaw, D. (1993). On setting unit thresholds in an incompletely connected associative net. Network, 4, 441–459.
Buzsaki, G. (1996). The hippocampo-neocortical dialog. Cereb. Cortex 6 (2), 81–92.
Frolov, A. A., Husek, D„ Muraviev, I. P. (1997). Informational capacity and recall quality in sparsely encoded Hopfield-like neural network: Analytical approaches and computer simulation. Neural Networks, 10, 845–855.
Hopfield, J.J. (1982). Neural network and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Science USA, 79, 2544–2548.
Kinzel, W (1985). Learning and pattern recognition in spin glass models. Z Physik, B 60, 205–213.
Sirota, A.M., Frolov, A.A., Husek D., Polyakov, P. Binary factorization in Hopfirld-like neural networks and hippocampal memory function. Neural Network World in print 2003.
Marr, D. (1971). Simple memory: a theory for archicor-tex. Phil. Trans. Roy. Soc. London B 262 24–81.
Rolls, R.T. (1989). The representation and storage of information in neural networks in the primate cerebral cortex and hippocampus. In R.Durbin, C. Miall and G. Mitchison, editors, The Computing Neuron. Addison-Weley, Reading, MA.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Wien
About this paper
Cite this paper
Frolov, A.A., Sirota, A.M., Husek, D., Muraviev, I., Combe, P. (2003). Binary Factorization in Hopfield-Like Neural Autoassociator: A Promising Tool for Data Compression. In: Pearson, D.W., Steele, N.C., Albrecht, R.F. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0646-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-7091-0646-4_12
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-00743-3
Online ISBN: 978-3-7091-0646-4
eBook Packages: Springer Book Archive