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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 Algorithms

Machine-mediated learning (ML), 2008
16 June 2008
Y. Goldberg
Yaácov Ritov
ArXiv (abs)PDFHTML

Papers citing "Local Procrustes for Manifold Embedding: A Measure of Embedding Quality and Embedding Algorithms"

11 / 11 papers shown
A Large-Scale Sensitivity Analysis on Latent Embeddings and
  Dimensionality Reductions for Text Spatializations
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations
Daniel Atzberger
Tim Cech
Willy Scheibel
Jürgen Döllner
M. Behrisch
Tobias Schreck
370
9
0
25 Jul 2024
ZADU: A Python Library for Evaluating the Reliability of Dimensionality
  Reduction Embeddings
ZADU: A Python Library for Evaluating the Reliability of Dimensionality Reduction EmbeddingsVisual .. (VISUAL), 2023
Hyeon Jeon
Aeri Cho
Jinhwa Jang
S. Lee
Jake Hyun
Hyung-Kwon Ko
Jaemin Jo
Jinwook Seo
363
26
0
01 Aug 2023
IAN: Iterated Adaptive Neighborhoods for manifold learning and
  dimensionality estimation
IAN: Iterated Adaptive Neighborhoods for manifold learning and dimensionality estimationNeural Computation (Neural Comput.), 2022
Luciano Dyballa
Steven W. Zucker
411
14
0
19 Aug 2022
Locally Linear Embedding and its Variants: Tutorial and Survey
Locally Linear Embedding and its Variants: Tutorial and Survey
Benyamin Ghojogh
A. Ghodsi
Fakhri Karray
Mark Crowley
260
33
0
22 Nov 2020
An Incremental Dimensionality Reduction Method for Visualizing Streaming
  Multidimensional Data
An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional DataIEEE Transactions on Visualization and Computer Graphics (IEEE TVCG), 2019
Takanori Fujiwara
Jia-Kai Chou
Shilpika Shilpika
Panpan Xu
Liu Ren
K. Ma
390
66
0
10 May 2019
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
117
6
0
16 Nov 2017
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
286
21
0
12 Dec 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
208
48
0
30 May 2013
On the Incommensurability Phenomenon
On the Incommensurability PhenomenonJournal of Classification (J. Classif.), 2013
D. E. Fishkind
Cencheng Shen
Youngser Park
Carey E. Priebe
444
5
0
09 Jan 2013
A new embedding quality assessment method for manifold learning
A new embedding quality assessment method for manifold learning
Peng Zhang
Yuanyuan Ren
Bo Zhang
205
33
0
08 Aug 2011
An Explicit Nonlinear Mapping for Manifold Learning
An Explicit Nonlinear Mapping for Manifold LearningIEEE Transactions on Cybernetics (IEEE Trans. Cybern.), 2010
Hong Qiao
Peng Zhang
Di Wang
Bo Zhang
346
95
0
15 Jan 2010
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