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Impact of signal-to-noise ratio and bandwidth on graph Laplacian
  spectrum from high-dimensional noisy point cloud
v1v2v3v4 (latest)

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

IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2020
21 November 2020
Xiucai Ding
Hau‐Tieng Wu
ArXiv (abs)PDFHTML

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

12 / 12 papers shown
Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy
Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature HierarchyInternational Conference on Learning Representations (ICLR), 2024
Ya-Wei Eileen Lin
Ronald R. Coifman
Zhengchao Wan
Ronen Talmon
606
9
0
28 Oct 2024
Boundary Detection Algorithm Inspired by Locally Linear Embedding
Boundary Detection Algorithm Inspired by Locally Linear Embedding
Pei-Cheng Kuo
Nan Wu
363
0
0
26 Jun 2024
Kernel spectral joint embeddings for high-dimensional noisy datasets using duo-landmark integral operators
Kernel spectral joint embeddings for high-dimensional noisy datasets using duo-landmark integral operators
Xiucai Ding
Rong Ma
414
4
0
20 May 2024
Design a Metric Robust to Complicated High Dimensional Noise for
  Efficient Manifold Denoising
Design a Metric Robust to Complicated High Dimensional Noise for Efficient Manifold Denoising
Hau-tieng Wu
DiffM
282
3
0
08 Jan 2024
Hyperbolic Diffusion Embedding and Distance for Hierarchical
  Representation Learning
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation LearningInternational Conference on Machine Learning (ICML), 2023
Ya-Wei Eileen Lin
Ronald R. Coifman
Zhengchao Wan
Ronen Talmon
290
25
0
30 May 2023
Augmentation Invariant Manifold Learning
Augmentation Invariant Manifold Learning
Shulei Wang
644
2
0
01 Nov 2022
Bi-stochastically normalized graph Laplacian: convergence to manifold
  Laplacian and robustness to outlier noise
Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noiseInformation and Inference A Journal of the IMA (JIII), 2022
Xiuyuan Cheng
Boris Landa
438
6
0
22 Jun 2022
Learning Low-Dimensional Nonlinear Structures from High-Dimensional
  Noisy Data: An Integral Operator Approach
Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator ApproachAnnals of Statistics (Ann. Stat.), 2022
Xiucai Ding
Rongkai Ma
512
17
0
28 Feb 2022
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned
  Datasets
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned DatasetsInternational Conference on Machine Learning (ICML), 2022
Tal Shnitzer
Mikhail Yurochkin
Kristjan Greenewald
Justin Solomon
275
9
0
03 Feb 2022
Spatiotemporal Analysis Using Riemannian Composition of Diffusion
  Operators
Spatiotemporal Analysis Using Riemannian Composition of Diffusion OperatorsApplied and Computational Harmonic Analysis (ACHA), 2022
Tal Shnitzer
Hau‐Tieng Wu
Ronen Talmon
198
11
0
21 Jan 2022
How do kernel-based sensor fusion algorithms behave under high
  dimensional noise?
How do kernel-based sensor fusion algorithms behave under high dimensional noise?
Xiucai Ding
Hau‐Tieng Wu
200
6
0
22 Nov 2021
Inferring manifolds using Gaussian processes
Inferring manifolds using Gaussian processes
David B. Dunson
Nan Wu
438
19
0
14 Oct 2021
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