Investigating Boolean Matrix Factorization
نویسندگان
چکیده
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 often 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 focus on the Boolean Matrix Factorization (BMF), introduce the task and present neural network, genetic algorithm and nonnegative matrix facrotization based BMF solvers. The algorithms are tested on several data sets and their results are compared.
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