Neural network attempt to nonlinear binary factor analysis of textual data
نویسندگان
چکیده
Possible application of a new procedure suitable of binary factorization of signals of large dimension and complexity is discussed. The new procedure is based on the search of attractors in Hoppfield-like associative memory. Starting from random initial state, network activity stabilizes in a attractor which corresponds to one of factors (a true attractor) or one of spurious attractors. Separation of true and spurious attractors is based on calculation of their Lyapunov function. Being applied to textual data the procedure conducted well and even more it showed sensitivity to the context in which the words were used.
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Neural network nonlinear factor analysis of high dimensional binary signals
Possible application of a new neural network suitable for binary factorization of signals of large dimension and complexity is introduced. We developed the new recall procedure of Hoppfield-like associative memory which allows search all attractors corresponding to factors (a true attrac-tor). Necessary separation of spurious attractors is based on calculation of their Lyapunov function. Being ...
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