Online EM for functional data

Abstract : A novel approach to perform unsupervised sequential learning for functional data is proposed. The goal is to extract reference shapes (referred to as templates) from noisy, deformed and censored realizations of curves and images. The proposed model generalizes the Bayesian dense deformable template model, a hierarchical model in which the template is the function to be estimated and the deformation is a nuisance, assumed to be random with a known prior distribution. The templates are estimated using a Monte Carlo version of the online Expectation–Maximization (EM) algorithm. The designed sequential inference framework is significantly more computationally efficient than equivalent batch learning algorithms, especially when the missing data is high-dimensional. Some numerical illustrations on curve registration problem and templates extraction from images are provided to support the methodology.
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Computational Statistics and Data Analysis, Elsevier, 2017, 111, pp.27 - 47. 〈10.1016/j.csda.2017.01.006〉
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Contributeur : Eric Moulines <>
Soumis le : mardi 19 décembre 2017 - 22:27:45
Dernière modification le : mercredi 28 mars 2018 - 14:06:02

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Florian Maire, Eric Moulines, Sidonie Lefebvre. Online EM for functional data. Computational Statistics and Data Analysis, Elsevier, 2017, 111, pp.27 - 47. 〈10.1016/j.csda.2017.01.006〉. 〈hal-01668241〉

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