Developing Genetic Algorithms for 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 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 background and initial version of Genetic Algorithm for binary matrix factorization.
منابع مشابه
WZ factorization via Abay-Broyden-Spedicato algorithms
Classes of Abaffy-Broyden-Spedicato (ABS) methods have been introduced for solving linear systems of equations. The algorithms are powerful methods for developing matrix factorizations and many fundamental numerical linear algebra processes. Here, we show how to apply the ABS algorithms to devise algorithms to compute the WZ and ZW factorizations of a nonsingular matrix as well as...
متن کاملInvestigating Boolean Matrix Factorization
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 re...
متن کاملA Projected Alternating Least square Approach for Computation of Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) is a common method in data mining that have been used in different applications as a dimension reduction, classification or clustering method. Methods in alternating least square (ALS) approach usually used to solve this non-convex minimization problem. At each step of ALS algorithms two convex least square problems should be solved, which causes high com...
متن کاملEvaluating Association Rules in Boolean Matrix Factorization
Association rules, or association rule mining, is a well-established and popular method of data mining and machine learning successfully applied in many different areas since mid-nineties. Association rules form a ground of the Asso algorithm for discovery of the first (presumably most important) factors in Boolean matrix factorization. In Asso, the confidence parameter of association rules hea...
متن کاملOn Finding Joint Subspace Boolean Matrix Factorizations
Finding latent factors of the data using matrix factorizations is a tried-and-tested approach in data mining. But finding shared factors over multiple matrices is more novel problem. Specifically, given two matrices, we want to find a set of factors shared by these two matrices and sets of factors specific for the matrices. Not only does such decomposition reveal what is common between the two ...
متن کامل