Think globally, fit locally under the Manifold Setup: Asymptotic Analysis of Locally Linear Embedding

12 March 2017

Papers citing "Think globally, fit locally under the Manifold Setup: Asymptotic Analysis of Locally Linear Embedding"

8 / 8 papers shown

Title
Manifold Learning with Sparse Regularised Optimal Transport Stephen X. Zhang Gilles Mordant Tetsuya Matsumoto Geoffrey Schiebinger OT 34 11 0 19 Jul 2023
Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach Xiucai Ding Rongkai Ma 49 9 0 28 Feb 2022
Avoiding unwanted results in locally linear embedding: A new understanding of regularization Liren Lin 11 1 0 28 Aug 2021
Locally Linear Embedding and its Variants: Tutorial and Survey Benyamin Ghojogh A. Ghodsi Fakhri Karray Mark Crowley 30 28 0 22 Nov 2020
Data-driven Efficient Solvers for Langevin Dynamics on Manifold in High Dimensions Yuan Gao Jiang Liu Nan Wu 21 12 0 22 May 2020
Scalability and robustness of spectral embedding: landmark diffusion is all you need Chao Shen Hau‐Tieng Wu 48 24 0 03 Jan 2020
Optimal Recovery of Precision Matrix for Mahalanobis Distance from High Dimensional Noisy Observations in Manifold Learning M. Gavish Ronen Talmon P. Su Hau‐Tieng Wu 30 8 0 19 Apr 2019
When Locally Linear Embedding Hits Boundary Hau‐Tieng Wu Nan Wu 19 11 0 11 Nov 2018