Bayesian sparse partial least squares.
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 dev...
| Main Authors: | , , , , |
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| Formato: | Journal article |
| Idioma: | English |
| Publicado em: |
Massachusetts Institute of Technology Press
2013
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| _version_ | 1826272408827330560 |
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| author | Vidaurre, D Gerven, v Bielza, C Larrañaga, P Heskes, T |
| author_facet | Vidaurre, D Gerven, v Bielza, C Larrañaga, P Heskes, T |
| author_sort | Vidaurre, D |
| collection | OXFORD |
| description | 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 estimation of the optimal number of latent components. We follow the variational approach to infer the parameter distributions. We have successfully tested the proposed methods on a synthetic data benchmark and on electrocorticogram data associated with several motor outputs in monkeys. |
| first_indexed | 2024-03-06T22:12:06Z |
| format | Journal article |
| id | oxford-uuid:5228a166-edc7-4a6e-a858-290ea2953d9f |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T22:12:06Z |
| publishDate | 2013 |
| publisher | Massachusetts Institute of Technology Press |
| record_format | dspace |
| spelling | oxford-uuid:5228a166-edc7-4a6e-a858-290ea2953d9f2022-03-26T16:23:58ZBayesian sparse partial least squares.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:5228a166-edc7-4a6e-a858-290ea2953d9fEnglishSymplectic Elements at OxfordMassachusetts Institute of Technology Press2013Vidaurre, DGerven, vBielza, CLarrañaga, PHeskes, TPartial 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 estimation of the optimal number of latent components. We follow the variational approach to infer the parameter distributions. We have successfully tested the proposed methods on a synthetic data benchmark and on electrocorticogram data associated with several motor outputs in monkeys. |
| spellingShingle | Vidaurre, D Gerven, v Bielza, C Larrañaga, P Heskes, T Bayesian sparse partial least squares. |
| title | Bayesian sparse partial least squares. |
| title_full | Bayesian sparse partial least squares. |
| title_fullStr | Bayesian sparse partial least squares. |
| title_full_unstemmed | Bayesian sparse partial least squares. |
| title_short | Bayesian sparse partial least squares. |
| title_sort | bayesian sparse partial least squares |
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