The sparse encoded Hopfield like neural network is modified to provide the Boolean factor analysis. New, more efficient method of sequential factor extraction, based on the characteristics behavior of the Lyapunov function is introduced. Efficiency of this attempt is shown not only on simulated data but on real data from Russian parliament but as well.
['A. A. Frolov', 'Dusan Husek', 'Pavel Polyakov', 'Hana Rezankova']
New Neural Network Based Approach Helps to Discover Hidden Russian Parliament Voting Patterns