Using dependencies to pair samples for multi-view learning

  • Authors:

  • Abhishek Tripathi;Arto Klami;Samuel Kaski

  • Affiliations:

  • University of Helsinki, Department of Computer Science, P.O.Box 68, 00014 UH, Finland;Helsinki University of Technology, Department of Information and Computer Science, P.O.Box 5400, 02015 TKK, Finland;Helsinki University of Technology, Department of Information and Computer Science, P.O.Box 5400, 02015 TKK, Finland

  • Venue:
  • ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
  • Year:
  • 2009

  • Graphical multi-way models

    ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I

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Abstract

Several data analysis tools such as (kernel) canonical correlation analysis and various multi-view learning methods require paired observations in two data sets. We study the problem of inferring such pairing for data sets with no known one-to-one pairing. The pairing is found by an iterative algorithm that alternates between searching for feature representations that reveal statistical dependencies between the data sets, and finding the best pairs for the samples. The method is applied on pairing probe sets of two different microarray platforms.