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1801.10108
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Error estimates for spectral convergence of the graph Laplacian on random geometric graphs towards the Laplace--Beltrami operator
30 January 2018
Nicolas García Trillos
Moritz Gerlach
Matthias Hein
D. Slepčev
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Papers citing
"Error estimates for spectral convergence of the graph Laplacian on random geometric graphs towards the Laplace--Beltrami operator"
50 / 106 papers shown
Title
Convergence of Laplacian Eigenmaps and its Rate for Submanifolds with Singularities
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Neural Operator: Learning Maps Between Function Spaces
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Anima Anandkumar
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Clustering dynamics on graphs: from spectral clustering to mean shift through Fokker-Planck interpolation
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Nicolas García Trillos
D. Slepčev
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Large sample spectral analysis of graph-based multi-manifold clustering
Nicolas García Trillos
Pengfei He
Chenghui Li
8
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28 Jul 2021
Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey
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A. Ghodsi
Fakhri Karray
Mark Crowley
15
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03 Jun 2021
Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs
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Error Bounds of the Invariant Statistics in Machine Learning of Ergodic Itô Diffusions
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J. Harlim
Xiantao Li
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21 May 2021
Kernel Two-Sample Tests for Manifold Data
Xiuyuan Cheng
Yao Xie
19
9
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07 May 2021
Robust Certification for Laplace Learning on Geometric Graphs
Matthew Thorpe
Bao Wang
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22
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22 Apr 2021
Which Sampling Densities are Suitable for Spectral Clustering on Unbounded Domains?
Henry-Louis de Kergorlay
26
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05 Apr 2021
Measure estimation on manifolds: an optimal transport approach
Vincent Divol
OT
16
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15 Feb 2021
LDLE: Low Distortion Local Eigenmaps
Dhruv Kohli
A. Cloninger
Gal Mishne
11
17
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26 Jan 2021
Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation
Xiuyuan Cheng
Nan Wu
63
29
0
25 Jan 2021
Manifold learning with arbitrary norms
Joe Kileel
Amit Moscovich
Nathan Zelesko
A. Singer
44
25
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28 Dec 2020
Impact of signal-to-noise ratio and bandwidth on graph Laplacian spectrum from high-dimensional noisy point cloud
Xiucai Ding
Hau‐Tieng Wu
6
13
0
21 Nov 2020
Convergence of Graph Laplacian with kNN Self-tuned Kernels
Xiuyuan Cheng
Hau‐Tieng Wu
8
23
0
03 Nov 2020
The Mathematical Foundations of Manifold Learning
Luke Melas-Kyriazi
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12
17
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30 Oct 2020
Product Manifold Learning
Sharon Zhang
Amit Moscovich
A. Singer
39
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19 Oct 2020
A Linear Transportation
L
p
\mathrm{L}^p
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p
Distance for Pattern Recognition
Oliver M. Crook
Mihai Cucuringu
Tim Hurst
Carola-Bibiane Schönlieb
Matthew Thorpe
K. Zygalakis
25
7
0
23 Sep 2020
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning
Vivien A. Cabannes
Loucas Pillaud-Vivien
Francis R. Bach
Alessandro Rudi
6
19
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09 Sep 2020
Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective
D. Sanz-Alonso
Ruiyi Yang
SSL
13
9
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26 Aug 2020
Posterior Consistency of Semi-Supervised Regression on Graphs
Andrea L. Bertozzi
Bamdad Hosseini
Hao Li
Kevin Miller
Andrew M. Stuart
10
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25 Jul 2020
Lipschitz regularity of graph Laplacians on random data clouds
Jeff Calder
Nicolas García Trillos
M. Lewicka
25
30
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13 Jul 2020
Consistency of Anchor-based Spectral Clustering
Henry-Louis de Kergorlay
D. Higham
4
6
0
24 Jun 2020
Construction and Monte Carlo estimation of wavelet frames generated by a reproducing kernel
Ernesto De Vito
Ž. Kereta
Valeriya Naumova
Lorenzo Rosasco
Stefano Vigogna
19
3
0
17 Jun 2020
Rates of Convergence for Laplacian Semi-Supervised Learning with Low Labeling Rates
Jeff Calder
D. Slepčev
Matthew Thorpe
18
26
0
04 Jun 2020
Spectral convergence of diffusion maps: improved error bounds and an alternative normalisation
Caroline L. Wormell
Sebastian Reich
8
37
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03 Jun 2020
Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to Heteroskedastic Noise
Boris Landa
Ronald R. Coifman
Y. Kluger
15
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31 May 2020
Data-driven Efficient Solvers for Langevin Dynamics on Manifold in High Dimensions
Yuan Gao
Jiang Liu
Nan Wu
21
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22 May 2020
Kernel Analog Forecasting: Multiscale Test Problems
Dmitry Burov
D. Giannakis
Krithika Manohar
Andrew M. Stuart
11
22
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13 May 2020
From graph cuts to isoperimetric inequalities: Convergence rates of Cheeger cuts on data clouds
Nicolas García Trillos
Ryan W. Murray
Matthew Thorpe
14
10
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20 Apr 2020
The SPDE Approach to Matérn Fields: Graph Representations
D. Sanz-Alonso
Ruiyi Yang
6
18
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16 Apr 2020
Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks
L. Cowen
K. Devkota
Xiaozhe Hu
James M. Murphy
Kaiyi Wu
9
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07 Mar 2020
Bridging data science and dynamical systems theory
Tyrus Berry
D. Giannakis
J. Harlim
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11
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18 Feb 2020
A continuum limit for the PageRank algorithm
Amber Yuan
Jeff Calder
Braxton Osting
34
18
0
24 Jan 2020
Scalability and robustness of spectral embedding: landmark diffusion is all you need
Chao Shen
Hau‐Tieng Wu
45
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0
03 Jan 2020
Spectral Convergence of Graph Laplacian and Heat Kernel Reconstruction in
L
∞
L^\infty
L
∞
from Random Samples
David B. Dunson
Hau‐Tieng Wu
Nan Wu
12
64
0
11 Dec 2019
Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms
Michael Perlmutter
Alexander Tong
F. Gao
Guy Wolf
M. Hirn
GNN
19
9
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14 Nov 2019
Spatially regularized active diffusion learning for high-dimensional images
James M. Murphy
11
12
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06 Nov 2019
Improved spectral convergence rates for graph Laplacians on epsilon-graphs and k-NN graphs
Jeff Calder
Nicolas García Trillos
35
40
0
29 Oct 2019
PDE-Inspired Algorithms for Semi-Supervised Learning on Point Clouds
Oliver M. Crook
Tim Hurst
Carola-Bibiane Schönlieb
Matthew Thorpe
K. Zygalakis
3DPC
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2
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23 Sep 2019
Spectral Analysis Of Weighted Laplacians Arising In Data Clustering
Franca Hoffmann
Bamdad Hosseini
Assad A. Oberai
Andrew M. Stuart
11
23
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Volume Doubling Condition and a Local Poincaré Inequality on Unweighted Random Geometric Graphs
F. Göbel
Gilles Blanchard
14
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Mumford-Shah functionals on graphs and their asymptotics
M. Caroccia
A. Chambolle
D. Slepčev
22
21
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Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods
Franca Hoffmann
Bamdad Hosseini
Zhi Ren
Andrew M. Stuart
14
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18 Jun 2019
Learning by Active Nonlinear Diffusion
Mauro Maggioni
James M. Murphy
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19
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Diffusion
K
K
K
-means clustering on manifolds: provable exact recovery via semidefinite relaxations
Xiaohui Chen
Yun Yang
24
16
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11 Mar 2019
Geometric structure of graph Laplacian embeddings
Nicolas García Trillos
Franca Hoffmann
Bamdad Hosseini
33
23
0
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A maximum principle argument for the uniform convergence of graph Laplacian regressors
Nicolas García Trillos
Ryan W. Murray
8
20
0
29 Jan 2019
When Locally Linear Embedding Hits Boundary
Hau‐Tieng Wu
Nan Wu
14
11
0
11 Nov 2018
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