Joint decompositions with flexible couplings

Rodrigo Cabral Farias (1) , Jérémy E Cohen (1) , Christian Jutten (2) , Pierre Comon (1)

1 GIPSA-CICS - GIPSA - Communication Information and Complex Systems
2 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing

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.

Mots clés: Tensor decompositions; coupled decompositions; data fu-sion; multimodal data

Domaines: Traitement du signal et de l'image [eess.SP]

Rodrigo Cabral Farias, Jérémy E Cohen, Christian Jutten, Pierre Comon. Joint decompositions with flexible couplings. 2015. ⟨hal-01135920v1⟩