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Binární faktorová analýza založená na neuronových sítích jako nástroj pro shlukování velkých datových souborů
Frolov, A. A. ; Húsek, Dušan ; Snášel, Václav ; Řezanková, H. ; Polyakov, P.Y.
The feature space transformation is a widely used method for data compression. Due to this transformation the original patterns are mapped into the space of features or factors of reduced dimensionality. In this paper we demonstrate that Hebbian learning in Hopfield-like neural network is a natural procedure for binary factorization. This paper is dedicated to estimation of the size of attraction basins around factors. Two global spurious attractors are shown to prevent convergence of the network activity to the factors invalidating any procedure of their search. These global attractors can be completely deleted from network dynamics by introducing a single inhibitory neuron with bi-directional Hebbian synapses. Due to additional inhibition, the size of attraction basins around factors becomes the same as around the stored patterns in usual Hopfield network.
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