Classification de Signaux Multidimensionnels Irrégulièrement Échantillonnés

Alexandre Constantin 1 Mathieu Fauvel 2 Stephane Girard 1 Serge Iovleff 3
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
3 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, CERIM - Santé publique : épidémiologie et qualité des soins-EA 2694, Polytech Lille - École polytechnique universitaire de Lille, Université de Lille, Sciences et Technologies
Abstract : A new statistical approach using Gaussian processes is proposed to to classify irregularly sampled signals without temporal rescaling. Moreover, the model offers a theoretical framework to impute missing values. First experiments on simulated data show promising results both in terms of classification and imputation accuracy. Good robustness properties with respect to the modelling assumptions are also observed.
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Poster communications
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Submitted on : Monday, September 2, 2019 - 2:16:07 PM
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Alexandre Constantin, Mathieu Fauvel, Stephane Girard, Serge Iovleff. Classification de Signaux Multidimensionnels Irrégulièrement Échantillonnés. 27e Colloque francophone de traitement du signal et des images - GRETSI 2019, Aug 2019, Lille, France. ⟨hal-02276255⟩



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