Comparison of neural network Boolean factor analysis method with some other dimension reduction methods on bars problem

Authors:
Dušan Húsek;Pavel Moravec;Václav Snášel;Alexander Frolov;Hana Řezanková;Pavel Polyakov
Affiliations:
Institute of Computer Science, Dept. of Neural Networks, Academy of Sciences of Czech Republic, Prague, Czech Republic;Department of Computer Science, FEECS, VŠB - Technical University of Ostrava, Ostrava, Poruba, Czech Republic;Department of Computer Science, FEECS, VŠB - Technical University of Ostrava, Ostrava, Poruba, Czech Republic;Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow, Russia;Department of Statistics and Probability, University of Economics, Prague, Czech Republic;Institute of Optical Neural Technologies, Russian Academy of Sciences, Moscow, Russia
Venue:
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Year:
2007

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Abstract

In this paper, we compare performance of novel neural network based algorithmfor Boolean factor analysiswith several dimension reduction techniques as a tool for feature extraction. Compared are namely singular value decomposition, semi-discrete decomposition and non-negative matrix factorization algorithms, including some cluster analysis methods as well. Even if the mainly mentioned methods are linear, it is interesting to compare them with neural network based Boolean factor analysis, because they arewell elaborated. Second reason for this is to show basic differences between Boolean and linear case. So called bars problem is used as the benchmark. Set of artificial signals generated as a Boolean sum of given number of bars is analyzed by thesemethods. Resulting images show that Boolean factor analysis is upmost suitable method for this kind of data.