Data Mining and Knowledge Discovery

, Volume 31, Issue 2, pp 350–370 | Cite as

Unsupervised group matching with application to cross-lingual topic matching without alignment information

  • Tomoharu Iwata
  • Motonobu Kanagawa
  • Tsutomu Hirao
  • Kenji Fukumizu
Article

Abstract

We propose a method for unsupervised group matching, which is the task of finding correspondence between groups across different domains without cross-domain similarity measurements or paired data. For example, the proposed method can find matching of topic categories in different languages without alignment information. The proposed method interprets a group as a probability distribution, which enables us to handle uncertainty in a limited amount of data, and to incorporate the high order information on groups. Groups are matched by maximizing the dependence between distributions, in which we use the Hilbert Schmidt independence criterion for measuring the dependence. By using kernel embedding which maps distributions into a reproducing kernel Hilbert space, we can calculate the dependence between distributions without density estimation. In the experiments, we demonstrate the effectiveness of the proposed method using synthetic and real data sets including an application to cross-lingual topic matching.

Keywords

Unsupervised object matching Kernel embedding of distributions Multilingual corpus analysis 

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Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Tomoharu Iwata
    • 1
  • Motonobu Kanagawa
    • 2
  • Tsutomu Hirao
    • 1
  • Kenji Fukumizu
    • 2
  1. 1.NTT Communication Science LaboratoriesKyotoJapan
  2. 2.The Institute of Statistical MathematicsTokyoJapan

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