A Riemannian geometric framework for manifold learning of non-Euclidean data

DOI10.1007/S11634-020-00426-3OpenAlexW3108701536WikidataQ114222065 ScholiaQ114222065MaRDI QIDQ2051579

Cheongjae Jang, Yung-Kyun Noh, Frank Chongwoo Park

Publication date: 24 November 2021

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-020-00426-3

zbMATH Keywords

Riemannian geometryharmonic mapdistortionmanifold learningnon-euclidean data

Mathematics Subject Classification ID

Integration on manifolds; measures on manifolds (58C35) Harmonic maps, etc. (58E20) Non-Euclidean differential geometry (53A35) Methods of local Riemannian geometry (53B21)

Related Items (1)

Symmetric positive definite manifold learning and its application in fault diagnosis

Cites Work

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• Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
• Harmonic Mappings of Riemannian Manifolds
• Nonparametric Regression between General Riemannian Manifolds

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