Parameterized low-rank binary matrix approximation.

• Fomin, Fedor V. ^[1] ; Golovach, Petr A. ^[1] ; Panolan, Fahad ^[2]
  1. [1] University of Bergen

     University of Bergen

     Noruega

     
  2. [2] Department of Computer Science and Engineering, IIT Hyderabad, Kandi, Sangareddy, Telangana, 502285, India
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 34, Nº 2, 2020, pág. 478
• Idioma: inglés
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• Resumen

  • Low-rank binary matrix approximation is a generic problem where one seeks a good approximation of a binary matrix by another binary matrix with some specific properties. A good approximation means that the difference between the two matrices in some matrix norm is small. The properties of the approximation binary matrix could be: a small number of different columns, a small binary rank or a small Boolean rank. Unfortunately, most variants of these problems are NP-hard. Due to this, we initiate the systematic algorithmic study of low-rank binary matrix approximation from the perspective of parameterized complexity. We show in which cases and under what conditions the problem is fixed-parameter tractable, admits a polynomial kernel and can be solved in parameterized subexponential time. [ABSTRACT FROM AUTHOR]