Implementing Boolean Matrix Factorization

Authors:
Roman Neruda;Václav Snášel;Jan Platoš;Pavel Krömer;Dušan Húsek;Alexander A. Frolov
Affiliations:
Institute of Computer Science, Dept. of Neural Networks, Academy of Sciences of Czech Republic, Prague, Czech Republic 182 07;Department of Computer Science, VŠB, Technical University of Ostrava, Ostrava, Czech Republic 708 33;Department of Computer Science, VŠB, Technical University of Ostrava, Ostrava, Czech Republic 708 33;Department of Computer Science, VŠB, Technical University of Ostrava, Ostrava, Czech Republic 708 33;Institute of Computer Science, Dept. of Neural Networks, Academy of Sciences of Czech Republic, Prague, Czech Republic 182 07;Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow, Russia 117 485
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Year:
2008

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Abstract

Matrix factorization or factor analysis is an important task helpful in the analysis of high dimensional real world data. There are several well known methods and algorithms for factorization of real data but many application areas including information retrieval, pattern recognition and data mining require processing of binary rather than real data. Unfortunately, the methods used for real matrix factorization fail in the latter case. In this paper we introduce the background of the task, neural network, genetic algorithm and non-negative matrix facrotization based solvers and compare the results obtained from computer experiments.