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Journal Neurocomputers №3 for 2014 г.
Article in number:
Combined algorithm for Boolean Factor Analysis based on neural network and likelihood maximization approaches
Keywords: boolean factor analysis attractor neural network likelihood function maximization KEGG database
Authors:
А. А. Frolov - Dr.Sc. (Eng.), Professor, Head of Laboratory, Institute of Higher Nervous Activity and Neurophysiology of RAS
D. Husek - Ph.D. (Eng.), Senior Research Scientist, Institute of Computer, Academy of Sciences, Prague, (Czech Republic)
P.Yu. Polyakov - Technical University of Ottawa (Czech Republic)
Abstract:
Boolean Factor Analysis (BFA) implies that signal components, factor loadings and factor scores are binary variables. In this study new approach to Boolean factor analysis (BFA) based on combining previously offered BFA method ANNIA (Attractor Neural Network with Increasing Activity) and likelihood maximization is described. We demonstrate the efficiency of new method when analyzing the KEGG database containing full genome sequencing of 1368 organisms.
Pages: 3-11
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