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Conference Papers Year : 2019

A Study of Boolean Matrix Factorization Under Supervised Settings

Abstract

Boolean matrix factorization is a generally accepted approach used in data analysis to explain data. It is commonly used under unsu-pervised setting or for data preprocessing under supervised settings. In this paper we study factors under supervised settings. We provide an experimental proof that factors are able to explain not only data as a whole but also classes in the data.
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Dates and versions

hal-02162929 , version 1 (23-06-2019)
hal-02162929 , version 2 (16-09-2019)

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Tatiana Makhalova, Martin Trnecka. A Study of Boolean Matrix Factorization Under Supervised Settings. ICFCA 2019 - 15th International Conference on Formal Concept Analysis, Jun 2019, Frankfurt, Germany. pp.341-348, ⟨10.1007/978-3-030-21462-3_24⟩. ⟨hal-02162929v2⟩
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