Manifold learning with bi-stochastic kernels

Author:

Marshall Nicholas F1,Coifman Ronald R1

Affiliation:

1. Program in Applied Mathematics, Yale University, New Haven, CT, USA

Publisher

Oxford University Press (OUP)

Subject

Applied Mathematics

Reference23 articles.

1. Towards a theoretical foundation for Laplacian-based manifold methods;Belkin;J. Comput. Syst. Sci.,2008

2. Embedding Riemannian manifolds by their heat kernel;Bérard, B.;Geom. Funct. Anal.,1994

3. Forecasting turbulent modes with nonparametric diffusion models: learning from noisy data;Berry;Phys. D,2016

4. Entropy minimization, DAD problems, and doubly stochastic kernels;Borwein;J. Funct. Anal.,1994

5. Function driven diffusion for personalized counterfactual inference;Cloninger,2017

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