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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 Approach

28 February 2022
Xiucai Ding
Rongkai Ma
ArXivPDFHTML

Papers citing "Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach"

4 / 4 papers shown
Title
IIKL: Isometric Immersion Kernel Learning with Riemannian Manifold for Geometric Preservation
IIKL: Isometric Immersion Kernel Learning with Riemannian Manifold for Geometric Preservation
Zihao Chen
Wenyong Wang
Jiachen Yang
Yu Xiang
29
0
0
07 May 2025
A Spectral Method for Assessing and Combining Multiple Data
  Visualizations
A Spectral Method for Assessing and Combining Multiple Data Visualizations
Rong Ma
Eric D. Sun
James Zou
16
11
0
25 Oct 2022
Theoretical Foundations of t-SNE for Visualizing High-Dimensional
  Clustered Data
Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data
T. Tony Cai
Rong Ma
19
108
0
16 May 2021
Understanding How Dimension Reduction Tools Work: An Empirical Approach
  to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
Yingfan Wang
Haiyang Huang
Cynthia Rudin
Yaron Shaposhnik
159
301
0
08 Dec 2020
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