Scalability and robustness of spectral embedding: landmark diffusion is all you need

3 January 2020

Papers citing "Scalability and robustness of spectral embedding: landmark diffusion is all you need"

6 / 6 papers shown

Title
Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy Y. Lin Ronald R. Coifman Gal Mishne Ronen Talmon 40 2 0 28 Oct 2024
SpecNet2: Orthogonalization-free spectral embedding by neural networks Ziyu Chen Yingzhou Li Xiuyuan Cheng 11 4 0 14 Jun 2022
Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach Xiucai Ding Rongkai Ma 23 9 0 28 Feb 2022
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 30 13 0 13 Oct 2021
Spectral Discovery of Jointly Smooth Features for Multimodal Data Felix Dietrich Or Yair Rotem Mulayoff Ronen Talmon Ioannis G. Kevrekidis 11 8 0 09 Apr 2020
Incomplete Pivoted QR-based Dimensionality Reduction A. Bermanis Aviv Rotbart Moshe Salhov Amir Averbuch 20 2 0 12 Jul 2016