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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APA
Makhalova, T., & Trnecka, M. (2019). A study of boolean matrix factorization under supervised settings. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11511 LNAI, pp. 341–348). Springer Verlag. https://doi.org/10.1007/978-3-030-21462-3_24
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