Linear latent force models using Gaussian Processes

Luengo García, David and Álvarez, Mauricio A. and Lawrence, Neil D. (2013). Linear latent force models using Gaussian Processes. "IEEE Transactions on Pattern Analysis and Machine Intelligence", v. 35 (n. 11); pp. 2693-2705. ISSN 0162-8828. https://doi.org/10.1109/TPAMI.2013.86.

Description

Title: Linear latent force models using Gaussian Processes
Author/s:
  • Luengo García, David
  • Álvarez, Mauricio A.
  • Lawrence, Neil D.
Item Type: Article
Título de Revista/Publicación: IEEE Transactions on Pattern Analysis and Machine Intelligence
Date: November 2013
ISSN: 0162-8828
Volume: 35
Subjects:
Freetext Keywords: Gaussian processes, dynamical systems, multitask learning, motion capture data, spatiotemporal covariances, differential equations
Faculty: E.U.I.T. Telecomunicación (UPM)
Department: Ingeniería de Circuitos y Sistemas [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (3MB) | Preview

Abstract

Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics.

More information

Item ID: 33207
DC Identifier: http://oa.upm.es/33207/
OAI Identifier: oai:oa.upm.es:33207
DOI: 10.1109/TPAMI.2013.86
Official URL: http://www.computer.org/csdl/trans/tp/2013/11/ttp2013112693-abs.html
Deposited by: Memoria Investigacion
Deposited on: 13 Apr 2015 19:08
Last Modified: 13 Apr 2015 19:08
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM