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Manifold Learning: The Price of Normalization

Manifold Learning: The Price of Normalization

Journal of machine learning research (JMLR), 2008
16 June 2008
Y. Goldberg
Alon Zakai
D. Kushnir
Yaácov Ritov
ArXiv (abs)PDFHTML

Papers citing "Manifold Learning: The Price of Normalization"

20 / 20 papers shown
AKRMap: Adaptive Kernel Regression for Trustworthy Visualization of Cross-Modal Embeddings
AKRMap: Adaptive Kernel Regression for Trustworthy Visualization of Cross-Modal Embeddings
Yilin Ye
Junchao Huang
Xingchen Zeng
Jiazhi Xia
Wei Zeng
500
1
0
20 May 2025
Manifold learning: what, how, and why
Manifold learning: what, how, and whyAnnual Review of Statistics and Its Application (ARSIA), 2023
M. Meilă
Hanyu Zhang
283
130
0
07 Nov 2023
Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction
Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction
Moritz Herrmann
Fabian Scheipl
198
7
0
22 Dec 2020
Minimax Estimation of Distances on a Surface and Minimax Manifold
  Learning in the Isometric-to-Convex Setting
Minimax Estimation of Distances on a Surface and Minimax Manifold Learning in the Isometric-to-Convex Setting
E. Arias-Castro
Phong Alain Chau
443
5
0
25 Nov 2020
Manifold Learning via Manifold Deflation
Manifold Learning via Manifold Deflation
Daniel Ting
Sai Li
175
3
0
07 Jul 2020
Selecting the independent coordinates of manifolds with large aspect
  ratios
Selecting the independent coordinates of manifolds with large aspect ratiosNeural Information Processing Systems (NeurIPS), 2019
Yu-Chia Chen
M. Meilă
244
17
0
02 Jul 2019
An Application of Manifold Learning in Global Shape Descriptors
An Application of Manifold Learning in Global Shape Descriptors
Fereshteh S. Bashiri
Reihaneh Rostami
P. Peissig
R. D'Souza
Zeyun Yu
3DV
92
5
0
08 Jan 2019
Perturbation Bounds for Procrustes, Classical Scaling, and
  Trilateration, with Applications to Manifold Learning
Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning
E. Arias-Castro
Adel Javanmard
Bruno Pelletier
220
26
0
22 Oct 2018
A New Method for Performance Analysis in Nonlinear Dimensionality
  Reduction
A New Method for Performance Analysis in Nonlinear Dimensionality Reduction
Jiaxi Liang
Shojaéddin Chenouri
C. Small
109
6
0
16 Nov 2017
Curvature-aware Manifold Learning
Curvature-aware Manifold Learning
Yangyang Li
153
18
0
22 Jun 2017
Non-Redundant Spectral Dimensionality Reduction
Non-Redundant Spectral Dimensionality Reduction
Yochai Blau
T. Michaeli
99
7
0
11 Dec 2016
On Clustering and Embedding Mixture Manifolds using a Low Rank
  Neighborhood Approach
On Clustering and Embedding Mixture Manifolds using a Low Rank Neighborhood ApproachIEEE Transactions on Geoscience and Remote Sensing (IEEE TGRS), 2016
A. Saranathan
M. Parente
316
3
0
23 Aug 2016
Manifold Matching using Shortest-Path Distance and Joint Neighborhood
  Selection
Manifold Matching using Shortest-Path Distance and Joint Neighborhood Selection
Cencheng Shen
Joshua T. Vogelstein
Carey E. Priebe
275
21
0
12 Dec 2014
Improved graph Laplacian via geometric self-consistency
Improved graph Laplacian via geometric self-consistencyNeural Information Processing Systems (NeurIPS), 2014
Dominique C. Perrault-Joncas
M. Meilă
292
19
0
31 May 2014
Non-linear dimensionality reduction: Riemannian metric estimation and
  the problem of geometric discovery
Non-linear dimensionality reduction: Riemannian metric estimation and the problem of geometric discovery
Dominique Perraul-Joncas
M. Meilă
198
48
0
30 May 2013
On the convergence of maximum variance unfolding
On the convergence of maximum variance unfoldingJournal of machine learning research (JMLR), 2012
E. Arias-Castro
Bruno Pelletier
339
18
0
31 Aug 2012
Co-clustering for directed graphs: the Stochastic co-Blockmodel and
  spectral algorithm Di-Sim
Co-clustering for directed graphs: the Stochastic co-Blockmodel and spectral algorithm Di-Sim
Karl Rohe
Tai Qin
Bin Yu
412
37
0
10 Apr 2012
Regression on manifolds: Estimation of the exterior derivative
Regression on manifolds: Estimation of the exterior derivative
A. Aswani
Peter J. Bickel
Claire Tomlin
427
75
0
08 Mar 2011
LLE with low-dimensional neighborhood representation
LLE with low-dimensional neighborhood representation
Y. Goldberg
Yaácov Ritov
289
2
0
06 Aug 2008
Local Procrustes for Manifold Embedding: A Measure of Embedding Quality
  and Embedding Algorithms
Local Procrustes for Manifold Embedding: A Measure of Embedding Quality and Embedding AlgorithmsMachine-mediated learning (ML), 2008
Y. Goldberg
Yaácov Ritov
371
58
0
16 Jun 2008
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