Binary Factorization in Hopfield-Like Neural Autoassociator: A Promising Tool for Data Compression

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.