Principal Boundary on Riemannian Manifolds

DOI10.1080/01621459.2019.1610660zbMath1441.62935arXiv1711.06705OpenAlexW2963991860WikidataQ115304197 ScholiaQ115304197MaRDI QIDQ5120679

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Publication date: 15 September 2020

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1711.06705

zbMATH Keywords

classificationmanifoldvector fieldSVMcovering ellipse balls

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Statistics on manifolds (62R30) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (2)

Weighted lens depth: Some applications to supervised classification ⋮ Principal component analysis and clustering on manifolds

Uses Software

• ElemStatLearn

Cites Work

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• Shape curves and geodesic modelling
• Analysis of principal nested spheres
• Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
• Fitting Smooth Paths to Speherical Data
• Principal Curves
• Principal Flows
• A Level Set Based Variational Principal Flow Method for Nonparametric Dimension Reduction on Riemannian Manifolds
• Shape-space smoothing splines for planar landmark data
• Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
• Principal component analysis for Riemannian manifolds, with an application to triangular shape spaces

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