Joint Decompositions with Flexible Couplings

  • Rodrigo Cabral Farias
  • Jérémy Emile Cohen
  • Christian Jutten
  • Pierre Comon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9237)

Abstract

A Bayesian framework is proposed to define flexible coupling models for joint decompositions of data sets. Under this framework, a solution to the joint decomposition can be cast in terms of a maximum a posteriori estimator. Examples of joint posterior distributions are provided, including general Gaussian priors and non Gaussian coupling priors. Then simulations are reported and show the effectiveness of this approach to fuse information from data sets, which are inherently of different size due to different time resolution of the measurement devices.

Keywords

Tensor decompositions Coupled decompositions Data fusion Multimodal data Heterogeneous data 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Rodrigo Cabral Farias
    • 1
  • Jérémy Emile Cohen
    • 1
  • Christian Jutten
    • 1
  • Pierre Comon
    • 1
  1. 1.GIPSA-Lab, UMR CNRS 5216, Grenoble CampusSaint Martin d’HèresFrance

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