Constructing Nonlinear Discriminants from Multiple Data Views

  title={Constructing Nonlinear Discriminants from Multiple Data Views},
  author={Tom Diethe and David R. Hardoon and John Shawe-Taylor},
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… CONTINUE READING
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