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.
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Neruda, R., Snášel, V., Platoš, J., Krömer, P., Húsek, D., Frolov, A.A. (2008). Implementing Boolean Matrix Factorization. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_56
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DOI: https://doi.org/10.1007/978-3-540-87536-9_56
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