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Communication Dans Un Congrès Année : 2023

Riemannian Locally Linear Embedding with Application to Kendall Shape Spaces

Résumé

Locally Linear Embedding is a dimensionality reduction method which relies on the conservation of barycentric alignments of neighbour points. It has been designed to learn the intrinsic structure of a set of points of a Euclidean space lying close to some submanifold. In this paper, we propose to generalise the method to manifold-valued data, that is a set of points lying close to some submanifold of a given manifold in which the points are modelled. We demonstrate our algorithm on some examples in Kendall shape spaces.
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Dates et versions

hal-04122754 , version 1 (08-06-2023)

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Paternité

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Elodie Maignant, Alain Trouvé, Xavier Pennec. Riemannian Locally Linear Embedding with Application to Kendall Shape Spaces. GSI 2023: Geometric Science of Information, Aug 2023, Saint-Malo, (France), France. pp.12-20, ⟨10.1007/978-3-031-38271-0_2⟩. ⟨hal-04122754⟩
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