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Combined algorithm for Boolean Factor Analysis based on neural network and likelihood maximization approaches


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)

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.

  1. Frolov A.A., Gusek D., Polyakov P.Ju. Bulevskij faktorny'j analiz na osnove attraktornoj nejronnoj seti i nekotory'e ego prilozheniya // Nejrokomp'yutery': razrabotka, primenenie. 2011. № 1. S. 25–46.
  2. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm // Journal of the Royal Statistical Society. Series B (Methodological)}. 1977. V. 39. № 1. P. 1–38.
  3. Foldiak P. Forming sparse representations by local anti-hebbian learning // Biological Cybernetics. 1990. V. 64. P. 165–170.
  4. Frolov A.A., Sirota A.M., Husek D., Muraviev I.P., Polyakov P.J. Binary factorization in Hopfield-like neural networks: single-step approximation and computer simulations // Neural Network Word. 2004. V. 14. P. 139–152.
  5. Frolov A.A., Husek D., Polyakov P., Rezankova H. New Neural Network Based Approach Helps to Discover Hidden Russian Parliament Voting Patterns // IEEE International Joint Conference on Neural Networks. 2006. P. 6518–6523.
  6. Frolov A.A., Husek D., Muraviev I.P., Polyakov P.Y. Boolean factor analysis by attractor neural network // IEEE Transactions on Neural Networks. 2007. V. 18. № 3. P. 698–707.
  7. Frolov A.A., Husek D., Rezankova H., Snasel V., Polyakov P. Clustering variables by classical approaches and neural network Boolean factor analysis // IEEE International Joint Conference on Neural Networks. 2008. P. 3742–3746.
  8. Frolov A.A., Husek D., Polyakov P.Y. Recurrent neural network based Boolean factor analysis and its application to automatic terms and documents categorization // IEEE Transactions on Neural Networks. 2009. V. 20. № 7. P. 1073–1086.
  9. Frolov A.A., Husek D., Polyakov P.Y. Estimation of Boolean factor analysis performance by informational gain // Proceedings of the 6th Atlantic Web Intelligence Conference (AWIC'2009). 2009. P. 83–94.
  10. Frolov A.A., Husek D., Polyakov P.Y. Origin and Elimination of Two Global Spurious Attractors in Hopfield-like Neural Network Performing Boolean Factor Analysis // Neurocomputing. 2010. V. 73. № 7–9. P. 1394–1404.
  11. Kanehisa M., Goto S., Kawashima S., Nakaya A. The KEGG databases at GenomeNet // Nucleic Acids Research. 2002. V. 30. № 1. P. 42.
  12. Kensche P.R., Noort V. Van, Dutilh B.E., Huynen M.A. Practical and theoretical advances in predicting the function of a protein by its phylogenetic distribution // Journal of the Royal Society Interface. 2008. V. 5. № 19. P. 151.
  13. Pellegrini M., Marcotte E.M., Thompson M.J., Eisenberg D., Yeates T.O. Assigning protein functions by comparative genome analysis: protein phylogenetic profiles // Proceedings of the National Academy of Sciences of the United States of America. 1999. V. 96. № 8. P. 4285.
  14. Ravasz E., Somera A.L., Mongru D.A., Oltvai Z.N., Barabґasi A.L. Hierarchical organization of modularity in metabolic networks // Science. 2002. V. 297. № 5586. P. 1551.
  15. Mering C. Von, Krause R., Snel B., Cornell M., Olive S.G.R, Fields S., Bork P. Comparative assessment of large-scale data sets of protein–protein interactions // Nature. 2002. V. 417. № 6887. P. 399–403.

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