Nonparametric Regression between General Riemannian Manifolds

DOI10.1137/080744189zbMath1195.41011OpenAlexW1981664955WikidataQ115247110 ScholiaQ115247110MaRDI QIDQ5747681

Florian Steinke, Matthias Hein, Bernhard Schölkopf

Publication date: 14 September 2010

Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/d455532e153df0396eda7a2c90c5f416e196667d

zbMATH Keywords

harmonic mapbiharmonic mapthin-plate splineregularized empirical risk minimizationeells energy

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10) Spline approximation (41A15) Manifolds and measure-geometric topics (49Q99)

Related Items (14)

About simple variational splines from the Hamiltonian viewpoint ⋮ Differentiable Piecewise-Bézier Surfaces on Riemannian Manifolds ⋮ Uniform convergence of local Fréchet regression with applications to locating extrema and time warping for metric space valued trajectories ⋮ Intrinsic Riemannian functional data analysis for sparse longitudinal observations ⋮ Nonlinear multi-output regression on unknown input manifold ⋮ Wasserstein Regression ⋮ Minimal time splines on the sphere ⋮ Robust Geodesic Regression ⋮ Intrinsic Riemannian functional data analysis ⋮ Fréchet regression for random objects with Euclidean predictors ⋮ Canonical Correlation Analysis on SPD(n) Manifolds ⋮ A Riemannian geometric framework for manifold learning of non-Euclidean data ⋮ Total variation regularized Fréchet regression for metric-space valued data ⋮ Nonparametric regression in nonstandard spaces

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