Learning the clustering of longitudinal shape data sets into a mixture of independent or branching trajectories

DOI10.1007/s11263-020-01337-8zbMath1483.68293OpenAlexW2973177992MaRDI QIDQ2056116

Stéphanie Allassonnière, Stanley Durrleman, Vianney Debavelaere

Publication date: 1 December 2021

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11263-020-01337-8

zbMATH Keywords

stochastic optimizationRiemannian manifoldstatistical modelmixture modellongitudinal data analysisbranching population

Mathematics Subject Classification ID

Statistics on manifolds (62R30) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

A hierarchical geodesic model for longitudinal analysis on manifolds ⋮ Multinomial principal component logistic regression on shape data ⋮ Sasaki metric for spline models of manifold-valued trajectories ⋮ A Coherent Framework for Learning Spatiotemporal Piecewise-Geodesic Trajectories from Longitudinal Manifold-Valued Data

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