Exploring multimodal data fusion through joint decompositions with flexible couplings

Rodrigo Cabral Farias 1 Jérémy E. Cohen 1 Pierre Comon 1
1 GIPSA-CICS - CICS
GIPSA-DIS - Département Images et Signal
Abstract : A Bayesian framework is proposed to define flexible coupling models for joint tensor decompositions of multiple data sets. Under this framework, a natural formulation of the data fusion problem is to cast it in terms of a joint maximum a posteriori (MAP) estimator. Data driven scenarii of joint posterior distributions are provided, including general Gaussian priors and non Gaussian coupling priors. We present and discuss implementation issues of algorithms used to obtain the joint MAP estimator. We also show how this framework can be adapted to tackle the problem of joint decompositions of large datasets. In the case of a conditional Gaussian coupling with a linear transformation, we give theoretical bounds on the data fusion performance using the Bayesian Cramer-Rao bound. Simulations are reported for hybrid coupling models ranging from simple additive Gaussian models, to Gamma-type models with positive variables and to the coupling of data sets which are inherently of different size due to different resolution of the measurement devices.
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IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2016, 64 (18), pp.4830-4844. 〈http://ieeexplore.ieee.org/document/7484711/〉. 〈10.1109/TSP.2016.2576425〉
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Contributeur : Rodrigo Cabral Farias <>
Soumis le : vendredi 29 mai 2015 - 14:45:10
Dernière modification le : lundi 9 avril 2018 - 12:22:48

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Rodrigo Cabral Farias, Jérémy E. Cohen, Pierre Comon. Exploring multimodal data fusion through joint decompositions with flexible couplings. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2016, 64 (18), pp.4830-4844. 〈http://ieeexplore.ieee.org/document/7484711/〉. 〈10.1109/TSP.2016.2576425〉. 〈hal-01158082〉

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