Summary
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.
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Extract
On the Road to Genetic Boolean Matrix Factorization
1. Introduction
In order to perform object recognition (no matter which one) it is necessary to learn representations of the underlying characteristic components. Such components correspond to object-parts, or features. These data sets may comprise discrete attributes, such as those from market basket analysis, information retrieval, and bioinformatics, as well as continuous attributes such as those in scientific simulations, astrophysical measurements, and sensor networks.The feature extraction if applied on binary datasets, addresses many research and application fields, such as association rule mining [1], market basket analysis [6], discovery of regulation patterns in DNA microarray experiments [30], etc. Many of these problem areas have been described in tests of PROXIMUS framework (e.g. [21]). So called bars problem [8, 31] is used as the benchmark. Set of artificial signals generated as a Boolean sum of given number of bars is analyzed ...See the full content of this document
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