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Bayesian sparse partial least squares

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  • Diego Vidaurre
  • Marcel A.J. Van Gerven, Radboud University Nijmegen
  • ,
  • Concha Bielza, Universidad Politécnia Madrid
  • ,
  • Pedro Larrañaga, Universidad Politécnia Madrid
  • ,
  • Tom Heskes, Radboud University Nijmegen

Partial least squares (PLS) is a class of methods that makes use of a set of latent or unobserved variables to model the relation between (typically) two sets of input and output variables, respectively. Several flavors, depending on how the latent variables or components are computed, have been developed over the last years. In this letter, we propose a Bayesian formulation of PLS alongwith some extensions. In a nutshell, we provide sparsity at the input space level and an automatic estimation of the optimal number of latent components. We follow the variational approach to infer the parameter distributions.We have successfully tested the proposedmethods on a synthetic data benchmark and on electrocorticogram data associated with several motor outputs in monkeys.

Original languageEnglish
JournalNeural Computation
Pages (from-to)3318-3339
Number of pages22
Publication statusPublished - 19 Nov 2013
Externally publishedYes

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