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A Study of Boolean Matrix Factorization Under Supervised Settings

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Formal Concept Analysis (ICFCA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11511))

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Abstract

Boolean matrix factorization is a generally accepted approach used in data analysis to explain data or for data preprocessing in the supervised settings. In this paper we study factors in the 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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Acknowledgment

The work of Tatiana Makhalova was supported by the Russian Science Foundation under grant 17- 11-01294 and performed at National Research University Higher School of Economics, Moscow, Russia.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Tatiana Makhalova

  2. LORIA, (CNRS – Inria – University of Lorraine), Vandœuvre-lès-Nancy, France

    Tatiana Makhalova

  3. Department of Computer Science, Palacký University Olomouc, Olomouc, Czech Republic

    Martin Trnecka

Authors
  1. Tatiana Makhalova
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  2. Martin Trnecka
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Corresponding author

Correspondence to Tatiana Makhalova .

Editor information

Editors and Affiliations

  1. Babeș-Bolyai University, Cluj-Napoca, Romania

    Diana Cristea

  2. University of Strasbourg, ENGEES, Strasbourg, France

    Florence Le Ber

  3. Frankfurt University of Applied Sciences, Frankfurt, Germany

    Baris Sertkaya

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Makhalova, T., Trnecka, M. (2019). A Study of Boolean Matrix Factorization Under Supervised Settings. In: Cristea, D., Le Ber, F., Sertkaya, B. (eds) Formal Concept Analysis. ICFCA 2019. Lecture Notes in Computer Science(), vol 11511. Springer, Cham. https://doi.org/10.1007/978-3-030-21462-3_24

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  • DOI: https://doi.org/10.1007/978-3-030-21462-3_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21461-6

  • Online ISBN: 978-3-030-21462-3

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