Abstract
One of the principles used for the transformation of original signal space into a space of lower dimension is a factor analysis based on the assumption that signals are random combinations of latent factors. The goal of the factor analysis is to find factors representation in the signal space (factor loadings) and the contributions of factors into the original signals (factor scores). Recently in [10] we have proposed the general method for Boolean factor analysis based on the Hopfield-like neural network. Due to the Hebbian learning rule the neurons of factor become connected more tightly than other neurons and hence factors can be revealed as attractors of the network dynamics by the random search. The peculiarity of usage the Hopfield-like network for Boolean factor analysis is the appearance of two global spurious attractors. They become dominant and, therefore, prevent successful factors search. To eliminate these attractors we propose a special unlearning procedure. The second unlearning procedure provides the suppression of factors with the largest attraction basins which dominate after suppression of global spurious attractors and prevent the recall of other factors. The origin of the global spurious attractors and the efficiency of the unlearning procedures are investigated in the present paper.
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Frolov, A.A., Húsek, D., Muraviev, I.P., Polyakov, P.Y. (2010). Learning and Unlearning in Hopfield-Like Neural Network Performing Boolean Factor Analysis. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning I. Studies in Computational Intelligence, vol 262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05177-7_26
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