Rank Matrix Factorisation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2015

AUTHORS

Thanh Le Van , Matthijs van Leeuwen , Siegfried Nijssen , Luc De Raedt

ABSTRACT

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. More... »

PAGES

734-746

References to SciGraph publications

  • 2014. Mining Rank Data in DISCOVERY SCIENCE
  • 2002-08. Revealing modular organization in the yeast transcriptional network in NATURE GENETICS
  • 1999-10. Learning the parts of objects by non-negative matrix factorization in NATURE
  • 2014. Ranked Tiling in MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES
  • 2009. Multiobjective Decomposition of Positive Integer Matrix: Application to Radiotherapy in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • Book

    TITLE

    Advances in Knowledge Discovery and Data Mining

    ISBN

    978-3-319-18037-3
    978-3-319-18038-0

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-18038-0_57

    DOI

    http://dx.doi.org/10.1007/978-3-319-18038-0_57

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1003409733


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