Neural Comput - Bayesian sparse partial least squares.

Tópicos

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Resumo

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.

Resumo Limpo

partial least squar pls class method make use set latent unobserv variabl model relat typic two set input output variabl respect sever flavor depend latent variabl compon comput develop last year letter propos bayesian formul pls along extens nutshel provid sparsiti input space level automat estim optim number latent compon follow variat approach infer paramet distribut success test propos method synthet data benchmark electrocorticogram data associ sever motor output monkey

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