Linear latent force models using Gaussian Processes


Autoria(s): Luengo García, David; Álvarez, Mauricio A.; Lawrence, Neil D.
Data(s)

01/11/2013

Resumo

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.

Formato

application/pdf

Identificador

http://oa.upm.es/33207/

Idioma(s)

eng

Publicador

E.U.I.T. Telecomunicación (UPM)

Relação

http://oa.upm.es/33207/1/INVE_MEM_2013_181081.pdf

http://www.computer.org/csdl/trans/tp/2013/11/ttp2013112693-abs.html

info:eu-repo/semantics/altIdentifier/doi/10.1109/TPAMI.2013.86

Direitos

http://creativecommons.org/licenses/by-nc-nd/3.0/es/

info:eu-repo/semantics/openAccess

Fonte

IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 2013-11, Vol. 35, No. 11

Palavras-Chave #Matemáticas #Robótica e Informática Industrial #Química
Tipo

info:eu-repo/semantics/article

Artículo

PeerReviewed