Tensor Factorization Using Auxiliary Information

  • Atsuhiro Narita
  • Kohei Hayashi
  • Ryota Tomioka
  • Hisashi Kashima
Conference paper

DOI: 10.1007/978-3-642-23783-6_32

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6912)
Cite this paper as:
Narita A., Hayashi K., Tomioka R., Kashima H. (2011) Tensor Factorization Using Auxiliary Information. In: Gunopulos D., Hofmann T., Malerba D., Vazirgiannis M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2011. Lecture Notes in Computer Science, vol 6912. Springer, Berlin, Heidelberg

Abstract

Most of the existing analysis methods for tensors (or multi-way arrays) only assume that tensors to be completed are of low rank. However, for example, when they are applied to tensor completion problems, their prediction accuracy tends to be significantly worse when only limited entries are observed. In this paper, we propose to use relationships among data as auxiliary information in addition to the low-rank assumption to improve the quality of tensor decomposition. We introduce two regularization approaches using graph Laplacians induced from the relationships, and design iterative algorithms for approximate solutions. Numerical experiments on tensor completion using synthetic and benchmark datasets show that the use of auxiliary information improves completion accuracy over the existing methods based only on the low-rank assumption, especially when observations are sparse.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Atsuhiro Narita
    • 1
  • Kohei Hayashi
    • 2
  • Ryota Tomioka
    • 1
  • Hisashi Kashima
    • 1
    • 3
  1. 1.Department of Mathematical InformaticsThe University of TokyoBunkyo-kuJapan
  2. 2.Graduate School of Information ScienceNara Institute of Science and TechnologyIkomaJapan
  3. 3.Basic Research Programs PRESTOSynthesis of Knowledge for Information Oriented SocietyJapan

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