Frobenius norm regularization for the multivariate von Misses distribution

Rodríguez Luján, Luis and Larrañaga Múgica, Pedro María and Bielza Lozoya, María Concepción (2016). Frobenius norm regularization for the multivariate von Misses distribution. "International Journal of Intelligent Systems", v. 32 (n. 2); pp. 153-176. ISSN 0884-8173. https://doi.org/10.1002/int.21834.

Description

Title: Frobenius norm regularization for the multivariate von Misses distribution
Author/s:
  • Rodríguez Luján, Luis
  • Larrañaga Múgica, Pedro María
  • Bielza Lozoya, María Concepción
Item Type: Article
Título de Revista/Publicación: International Journal of Intelligent Systems
Date: 2016
ISSN: 0884-8173
Volume: 32
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Penalizing the model complexity is necessary to avoid overfittingwhen the number of data samples is low with respect to the number of model parameters. In this paper, we introduce a penalization term that places an independent prior distribution for each parameter of the multivariate von Mises distribution.We also propose a circular distance that can be used to estimate the Kullback–Leibler divergence between any two circular distributions as goodness-of-fit measure. We compare the resulting regularized von Mises models on synthetic data and real neuroanatomical data to show that the distribution fitted using the penalized estimator generally achieves better results than nonpenalized multivariate von Mises estimator.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainC080020-09UnspecifiedUniversidad Politécnica de MadridCajal Blue Brain Project
FP7604102HBPUnspecifiedThe Human Brain Project
Madrid Regional GovernmentS2013/ICE-2845CASI-CAM-CMUniversidad Carlos IIIConceptos y aplicaciones de los sistemas inteligentes
Government of SpainFPU014/04818UnspecifiedUnspecifiedUnspecified
Government of SpainTIN2013-41592-PUnspecifiedUnspecifiedAprendizaje de redes bayesianas con variables sin y con direccionalidad para descubrimiento de asociaciones, predicción multirespuesta y clustering

More information

Item ID: 46294
DC Identifier: http://oa.upm.es/46294/
OAI Identifier: oai:oa.upm.es:46294
DOI: 10.1002/int.21834
Official URL: http://onlinelibrary.wiley.com/doi/10.1002/int.21834/abstract
Deposited by: Memoria Investigacion
Deposited on: 06 Nov 2017 08:42
Last Modified: 22 Mar 2019 15:39
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