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Publication Detail
Constructing nonlinear discriminants from multiple data views
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Conference Proceeding
  • Authors:
    Diethe T, Hardoon DR, Shawe-Taylor J
  • Publication date:
    05/11/2010
  • Pagination:
    328, 343
  • Journal:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    6321 LNAI
  • Issue:
    PART 1
  • Status:
    Published
  • Print ISSN:
    0302-9743
Abstract
There are many situations in which we have more than one view of a single data source, or in which we have multiple sources of data that are aligned. We would like to be able to build classifiers which incorporate these to enhance classification performance. Kernel Fisher Discriminant Analysis (KFDA) can be formulated as a convex optimisation problem, which we extend to the Multiview setting (MFDA) and introduce a sparse version (SMFDA). We show that our formulations are justified from both probabilistic and learning theory perspectives. We then extend the optimisation problem to account for directions unique to each view (PMFDA). We show experimental validation on a toy dataset, and then give experimental results on a brain imaging dataset and part of the PASCAL 2007 VOC challenge dataset. © 2010 Springer-Verlag Berlin Heidelberg.
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