Impact of signal-to-noise ratio and bandwidth on graph Laplacian
spectrum from high-dimensional noisy point cloud

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Impact of signal-to-noise ratio and bandwidth on graph Laplacian spectrum from high-dimensional noisy point cloud

21 November 2020

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Papers citing "Impact of signal-to-noise ratio and bandwidth on graph Laplacian spectrum from high-dimensional noisy point cloud"

6 / 6 papers shown

Title
Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy Ya-Wei Eileen Lin Ronald R. Coifman Zhengchao Wan Ronen Talmon 195 3 0 28 Oct 2024
Kernel spectral joint embeddings for high-dimensional noisy datasets using duo-landmark integral operators Xiucai Ding Rong Ma 98 2 0 20 May 2024
Augmentation Invariant Manifold Learning Shulei Wang 244 1 0 01 Nov 2022
Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach Xiucai Ding Rongkai Ma 95 9 0 28 Feb 2022
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets Tal Shnitzer Mikhail Yurochkin Kristjan Greenewald Justin Solomon 90 6 0 03 Feb 2022
Inferring Manifolds From Noisy Data Using Gaussian Processes David B. Dunson Nan Wu 91 18 0 14 Oct 2021