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Communication Dans Un Congrès Année : 2016

Non-negative decomposition of linear relationships: application to multi-source ocean remote sensing data

Résumé

The identification and separation of contributions associated with different sources or processes is a general problem in signal and image processing. Here, we focus on the decomposition of multiple linear relationships and introduce a non-negative formulation. The proposed models can be viewed as generalizations of latent class regression models and account for possibly varying magnitudes of the linear transfer functions. Along with these models, we present model calibration algorithms. We first demonstrate their performance on simulated data. We also report an application to the analysis of upper ocean dynamics from remote sensing data (namely, satellite-derived Sea Surface Height (SSH) and Sea Surface temperature (SST) image series). This application further stresses the proposed formulation's relevance compared to state-of-the-art regression models.
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Dates et versions

hal-01298201 , version 1 (05-04-2016)

Identifiants

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Manuel Lopez Radcenco, Abdeldjalil Aissa El Bey, Pierre Ailliot, Pierre Tandeo, Ronan Fablet. Non-negative decomposition of linear relationships: application to multi-source ocean remote sensing data. 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), Mar 2016, Shanghai, China. pp.4179-4183, ⟨10.1109/ICASSP.2016.7472464⟩. ⟨hal-01298201⟩
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