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Gaussian Process Classification Using Posterior Linearization

Garcia-Fernandez, Angel F ; Tronarp, Filip LU and Särkkä, Simo (2019) In IEEE Signal Processing Letters 26(5). p.735-739
Abstract
This letter proposes a new algorithm for Gaussian process classification based on posterior linearization (PL). In PL, a Gaussian approximation to the posterior density is obtained iteratively using the best possible linearization of the conditional mean of the labels and accounting for the linearization error. PL has some theoretical advantages over expectation propagation (EP): all calculated covariance matrices are positive definite and there is a local convergence theorem. In experimental data, PL has better performance than EP with the noisy threshold likelihood and the parallel implementation of the algorithms.
Please use this url to cite or link to this publication:
author
; and
publishing date
type
Contribution to journal
publication status
published
subject
in
IEEE Signal Processing Letters
volume
26
issue
5
pages
735 - 739
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85064572097
ISSN
1070-9908
DOI
10.1109/LSP.2019.2906929
language
English
LU publication?
no
id
f4c05d8a-ae81-429d-8443-8819789fcce9
date added to LUP
2023-08-20 22:46:58
date last changed
2023-11-10 10:56:01
@article{f4c05d8a-ae81-429d-8443-8819789fcce9,
  abstract     = {{This letter proposes a new algorithm for Gaussian process classification based on posterior linearization (PL). In PL, a Gaussian approximation to the posterior density is obtained iteratively using the best possible linearization of the conditional mean of the labels and accounting for the linearization error. PL has some theoretical advantages over expectation propagation (EP): all calculated covariance matrices are positive definite and there is a local convergence theorem. In experimental data, PL has better performance than EP with the noisy threshold likelihood and the parallel implementation of the algorithms.}},
  author       = {{Garcia-Fernandez, Angel F and Tronarp, Filip and Särkkä, Simo}},
  issn         = {{1070-9908}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{735--739}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Signal Processing Letters}},
  title        = {{Gaussian Process Classification Using Posterior Linearization}},
  url          = {{http://dx.doi.org/10.1109/LSP.2019.2906929}},
  doi          = {{10.1109/LSP.2019.2906929}},
  volume       = {{26}},
  year         = {{2019}},
}