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2001.00801
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Scalability and robustness of spectral embedding: landmark diffusion is all you need
Information and Inference A Journal of the IMA (JIII), 2020
3 January 2020
Chao Shen
Hau‐Tieng Wu
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Papers citing
"Scalability and robustness of spectral embedding: landmark diffusion is all you need"
17 / 17 papers shown
Neumann eigenmaps for landmark embedding
International Conference on Sampling Theory and Applications (SampTA), 2025
Shashank Sule
Wojciech Czaja
233
0
0
10 Feb 2025
Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy
International Conference on Learning Representations (ICLR), 2024
Ya-Wei Eileen Lin
Ronald R. Coifman
Zhengchao Wan
Ronen Talmon
606
9
0
28 Oct 2024
Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets
Boris Landa
Y. Kluger
Rong Ma
281
1
0
01 Jul 2024
Boundary Detection Algorithm Inspired by Locally Linear Embedding
Pei-Cheng Kuo
Nan Wu
362
0
0
26 Jun 2024
Kernel spectral joint embeddings for high-dimensional noisy datasets using duo-landmark integral operators
Xiucai Ding
Rong Ma
413
4
0
20 May 2024
Landmark Alternating Diffusion
Sing-Yuan Yeh
Hau-tieng Wu
Ronen Talmon
Mao-Pei Tsui
158
2
0
29 Apr 2024
Design a Metric Robust to Complicated High Dimensional Noise for Efficient Manifold Denoising
Hau-tieng Wu
DiffM
279
3
0
08 Jan 2024
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning
International Conference on Machine Learning (ICML), 2023
Ya-Wei Eileen Lin
Ronald R. Coifman
Zhengchao Wan
Ronen Talmon
290
25
0
30 May 2023
Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise
Information and Inference A Journal of the IMA (JIII), 2022
Xiuyuan Cheng
Boris Landa
435
6
0
22 Jun 2022
SpecNet2: Orthogonalization-free spectral embedding by neural networks
Mathematical and Scientific Machine Learning (MSML), 2022
Ziyu Chen
Yingzhou Li
Xiuyuan Cheng
218
7
0
14 Jun 2022
Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach
Annals of Statistics (Ann. Stat.), 2022
Xiucai Ding
Rongkai Ma
511
17
0
28 Feb 2022
Inferring manifolds using Gaussian processes
David B. Dunson
Nan Wu
436
19
0
14 Oct 2021
On the Parameter Combinations That Matter and on Those That do Not
N. Evangelou
Noah J. Wichrowski
George A. Kevrekidis
Felix Dietrich
M. Kooshkbaghi
Sarah McFann
Ioannis G. Kevrekidis
306
22
0
13 Oct 2021
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
Xiucai Ding
Hau‐Tieng Wu
686
19
0
21 Nov 2020
Convergence of Graph Laplacian with kNN Self-tuned Kernels
Xiuyuan Cheng
Hau‐Tieng Wu
362
28
0
03 Nov 2020
Graph Based Gaussian Processes on Restricted Domains
David B. Dunson
Hau‐Tieng Wu
Nan Wu
GP
328
29
0
14 Oct 2020
Spectral Discovery of Jointly Smooth Features for Multimodal Data
SIAM Journal on Mathematics of Data Science (SIMODS), 2020
Felix Dietrich
Or Yair
Rotem Mulayoff
Ronen Talmon
Ioannis G. Kevrekidis
332
10
0
09 Apr 2020
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