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Bayesian sparse partial least squares.

Abstract:

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 along with some extensions. In a nutshell, we provide sparsity at the input space level and an automatic estimat...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1162/neco_a_00524

Authors


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Institution:
University of Oxford
Department:
Oxford, MSD, Psychiatry
van Gerven, MA More by this author
Larrañaga, P More by this author
Publisher:
Massachusetts Institute of Technology Press Publisher's website
Journal:
Neural computation Journal website
Volume:
25
Issue:
12
Pages:
3318-3339
Publication date:
2013-12-05
DOI:
EISSN:
1530-888X
ISSN:
0899-7667
URN:
uuid:5228a166-edc7-4a6e-a858-290ea2953d9f
Source identifiers:
431282
Local pid:
pubs:431282

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