Rank Matrix Factorisation

  • Thanh Le Van
  • Matthijs van Leeuwen
  • Siegfried Nijssen
  • Luc De Raedt
Conference paper

DOI: 10.1007/978-3-319-18038-0_57

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9077)
Cite this paper as:
Le Van T., van Leeuwen M., Nijssen S., De Raedt L. (2015) Rank Matrix Factorisation. In: Cao T., Lim EP., Zhou ZH., Ho TB., Cheung D., Motoda H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science, vol 9077. Springer, Cham

Abstract

We introduce the problem of rank matrix factorisation (RMF). That is, we consider the decomposition of a rank matrix, in which each row is a (partial or complete) ranking of all columns. Rank matrices naturally appear in many applications of interest, such as sports competitions. Summarising such a rank matrix by two smaller matrices, in which one contains partial rankings that can be interpreted as local patterns, is therefore an important problem.

After introducing the general problem, we consider a specific instance called Sparse RMF, in which we enforce the rank profiles to be sparse, i.e., to contain many zeroes. We propose a greedy algorithm for this problem based on integer linear programming. Experiments on both synthetic and real data demonstrate the potential of rank matrix factorisation.

Keywords

Matrix factorisation Rank data Integer linear programming 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Thanh Le Van
    • 1
  • Matthijs van Leeuwen
    • 1
  • Siegfried Nijssen
    • 1
    • 2
  • Luc De Raedt
    • 1
  1. 1.Department of Computer ScienceKU LeuvenLeuvenBelgium
  2. 2.Leiden Institute for Advanced Computer ScienceUniversiteit LeidenLeidenThe Netherlands

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