Fetching the paper…
Reading the bibliography…
Multidimensional Scaling (MDS) is one of the first fundamental manifold learning methods.
Eigenvalue and generalized eigenvalue problems: Tutorial
Ghojogh, Benyamin, Karray, Fakhri, and Crowley, Mark · 1903
Earlier work this paper cites.
Feature selection and feature extraction in pattern analysis: A literature review
Ghojogh, Benyamin, Samad, Maria N, Mashhadi, Sayema Asif, Kapoor, Tania, Ali, Wahab, Karray, Fakhri, and Crowley, Mark · 1905
Earlier work this paper cites.
Roweis discriminant analysis: A generalized subspace learning method
Ghojogh, Benyamin, Karray, Fakhri, and Crowley, Mark · 1910
Earlier work this paper cites.
Multidimensional scaling: I. theory and method
Torgerson, Warren S · 1952
Earlier work this paper cites.
Multidimensional scaling of similarity
Torgerson, Warren S · 1965
Earlier work this paper cites.
Some distance properties of latent root and vector methods used in multivariate analysis
Gower, John C · 1966
Earlier work this paper cites.
Foundations of multidimensional scaling
Beals, Richard, Krantz, David H, and Tversky, Amos · 1968
Earlier work this paper cites.
A nonlinear mapping for data structure analysis
Sammon, John W · 1969
Earlier work this paper cites.
Smallest space analysis of intelligence and achievement tests
Schlesinger, ItzchakM and Guttman, Louis · 1969
Earlier work this paper cites.
Some properties of clasical multi-dimesional scaling
Mardia, Kanti V · 1978
Earlier work this paper cites.
The analytical solution of the additive constant problem
Cailliez, Francis · 1983
Earlier work this paper cites.
Multidimensional scaling of emotional facial expressions: similarity from preschoolers to adults
Russell, James A and Bullock, Merry · 1985
Earlier work this paper cites.
Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets
Faloutsos, Christos and Lin, King-Ip · 1995
Earlier work this paper cites.
The classification of facial expressions of emotion: A multidimensional-scaling approach
Katsikitis, Mary · 1997
Earlier work this paper cites.
Evaluating a class of distance-mapping algorithms for data mining and clustering
Wang, Jason Tsong-Li, Wang, Xiong, Lin, King-Ip, Shasha, Dennis, Shapiro, Bruce A, and Zhang, Kaizhong · 1999
Earlier work this paper cites.
A global geometric framework for nonlinear dimensionality reduction
Tenenbaum, Joshua B, De Silva, Vin, and Langford, John C · 2000
Earlier work this paper cites.
A course in differential geometry , volume 27
Aubin, Thierry · 2001
Earlier work this paper cites.
Using the Nyström method to speed up kernel machines
Williams, Christopher KI and Seeger, Matthias · 2001
Earlier work this paper cites.
Curvilinear distance analysis versus isomap
Lee, John Aldo, Lendasse, Amaury, Verleysen, Michel, et al · 2002
Cited alongside, same era.
Global versus local methods in nonlinear dimensionality reduction
De Silva, Vin and Tenenbaum, Joshua B · 2003
Cited alongside, same era.
Think globally, fit locally: unsupervised learning of low dimensional manifolds
Saul, Lawrence K and Roweis, Sam T · 2003
Cited alongside, same era.
Kernel Isomap
Choi, Heeyoul and Choi, Seungjin · 2004
Cited alongside, same era.
Sparse multidimensional scaling using landmark points
De Silva, Vin and Tenenbaum, Joshua B · 2004
Cited alongside, same era.
A kernel view of the dimensionality reduction of manifolds
Ham, Jihun, Lee, Daniel D, Mika, Sebastian, and Schölkopf, Bernhard · 2004
Cited alongside, same era.
Non-metric multidimensional scaling (mds)
Holland, Steven M · 2008
Later among the works it cites.
Large-scale manifold learning
Talwalkar, Ameet, Kumar, Sanjiv, and Rowley, Henry · 2008
Later among the works it cites.
Introduction to algorithms
Cormen, Thomas H, Leiserson, Charles E, Rivest, Ronald L, and Stein, Clifford · 2009
Later among the works it cites.
Multidimensional scaling
De Leeuw, Jan · 2011
Later among the works it cites.
A generalised solution to the out-of-sample extension problem in manifold learning
Strange, Harry and Zwiggelaar, Reyer · 2011
Later among the works it cites.
Facial expression recognition based on local binary patterns and kernel discriminant Isomap
Zhao, Xiaoming and Zhang, Shiqing · 2011
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
An extended Isomap algorithm for learning multi-class manifold
Wu, Yiming and Chan, Kap Luk · 2004
Cited alongside, same era.
Modern multidimensional scaling: Theory and applications
Borg, Ingwer and Groenen, Patrick JF · 2005
Cited alongside, same era.
Kernel Isomap on noisy manifold
Choi, Heeyoul and Choi, Seungjin · 2005
Cited alongside, same era.
Fastmap, metricmap, and landmark mds are all nystrom algorithms
Platt, John · 2005
Cited alongside, same era.
Spectral dimensionality reduction
Bengio, Yoshua, Delalleau, Olivier, Le Roux, Nicolas, Paiement, Jean-François, Vincent, Pascal, and Ouimet, Marie · 2006
Cited alongside, same era.
Dimensionality reduction a short tutorial
Ghodsi, Ali · 2006
Cited alongside, same era.
A general framework for dimensionality-reducing data visualization mapping
Bunte, Kerstin, Biehl, Michael, and Hammer, Barbara · 2012
Later among the works it cites.
Out-of-sample kernel extensions for nonparametric dimensionality reduction
Gisbrecht, Andrej, Lueks, Wouter, Mokbel, Bassam, and Hammer, Barbara · 2012
Later among the works it cites.
Lecture: Multidimensional scaling, advanced applied multivariate analysis
Jung, Sungkyu · 2013
Later among the works it cites.
Multidimensional scaling: History, theory, and applications
Young, Forrest W · 2013
Later among the works it cites.
Open Problems in Spectral Dimensionality Reduction
Strange, Harry and Zwiggelaar, Reyer · 2014
Later among the works it cites.
Parametric nonlinear dimensionality reduction using kernel t-sne
Gisbrecht, Andrej, Schulz, Alexander, and Hammer, Barbara · 2015
Later among the works it cites.
Literature survey on low rank approximation of matrices
Kishore Kumar, N and Schneider, Jan · 2017
Later among the works it cites.
Lecture: Recasting principal components
Oldford, Wayne · 2018
Later among the works it cites.
Unsupervised and supervised principal component analysis: Tutorial
Ghojogh, Benyamin and Crowley, Mark · 2019
Later among the works it cites.
MNIST handwritten digits dataset
LeCun, Yann, Cortes, Corinna, and Burges, Christopher J.C · 2019
Later among the works it cites.
Ghojogh, Benyamin, Karray, Fakhri, and Crowley, Mark · 2020
Closest in time.