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R. Sibson, Studies in robustness of multidimensional-scaling: Procrustes statistics, Journal of the Royal Statistical Society Series B - Methodological 40 (2) (1978) 234–238
1978
Earlier work this paper cites.
R. Sibson, Perturbational analysis of classical scaling, Journal of the Royal Statistical Society Series B - Methodological 41 (2) (1979) 217–229
1979
Earlier work this paper cites.
M. Turk, A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience 3 (1) (1991) 71–86
1991
Earlier work this paper cites.
J. R. Magnus, H. Neudecker, Matrix differential calculus with applications in statistics and econometrics, 2nd Edition, John Wiley & Sons, 1999
1999
Earlier work this paper cites.
D. L. Donoho, High-dimensional data analysis: the curses and blessings of dimensionality, in: Proceedings of American Mathematical Society Conference on Math Challenges of the 21st Century, 2000
2000
Earlier work this paper cites.
T. F. Cox, M. A. A. Cox, Multidimensional Scaling, Second Edition, Chapman & Hall, 2000
2000
Earlier work this paper cites.
S. T. Roweis, L. K. Saul, Nonlinear dimensionality reduction by locally linear embedding, Science 290 (5500) (2000) 2323–2326
2000
Earlier work this paper cites.
J. B. Tenenbaum, V. Silva, J. C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science 290 (5500) (2000) 2319–2323
2000
Earlier work this paper cites.
I. T. Jolliffe, Principal Component Analysis, 2nd Edition, Springer, 2002
2002
Earlier work this paper cites.
O. Kouropteva, O. Okun, M. Pietik a ¨ \ddot{\mbox{a}} inen, Selection of the optimal parameter value for the locally linear embedding algorithm, in: The 1st International Conference on Fuzzy Systems and Knowledge Discovery, 2002, pp. 359–363
2002
Earlier work this paper cites.
L. K. Saul, S. T. Roweis, Think globally, fit locally: Unsupervised learning of low dimensional manifolds, Journal of Machine Learning Research 4 (2003) 119–155
2003
Earlier work this paper cites.
V. De Silva, J. B. Tenenbaum, Global versus local methods in nonlinear dimensionality reduction, in: Advances in Neural Information Processing Systems 15, Vol. 15, 2003, pp. 705–712
2003
Earlier work this paper cites.
M. Belkin, Problems of learning on manifolds, Ph.D. thesis, The University of Chicago (2003)
2003
Earlier work this paper cites.
M. Belkin, P. Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Computation 15 (6) (2003) 1373–1396
2003
Earlier work this paper cites.
D. L. Donoho, C. Grimes, Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data, Proceedings of the National Academy of Sciences of the United States of America 100 (10) (2003) 5591–5596
2003
Earlier work this paper cites.
G. A. F. Seber, Multivariate observations, John Wiley & Sons. INC, 2004
2004
Cited alongside, same era.
R. R. Coifman, S. Lafon, A. B. Lee, M. Maggioni, B. Nadler, F. Warner, S. W. Zucker, Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps, Proceedings of the National Academy of Sciences of the United States of America 102 (21) (2005) 7426–7431
2005
Cited alongside, same era.
Z. Zhang, H. Zha, Principal manifolds and nonlinear dimensionality reduction via tangent space alignment, SIAM Journal on Scientific Computing 26 (1) (2005) 313–338
2005
Cited alongside, same era.
C. M. Bachmann, T. L. Ainsworth, R. A. Fusina, Exploiting manifold geometry in hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing 43 (3) (2005) 441–454
2005
Cited alongside, same era.
J. Dattorro, Convex Optimization & Euclidean Distance Geometry, Meboo Publishing USA, 2005
P.-A. Absil, R. Mahony, R. Sepulchre, Optimization Algorithms on Matrix Manifolds, Princeton University Press, Princeton, NJ, USA, 2007
2007
Later among the works it cites.
T. Lin, H. Zha, Riemannian manifold learning, IEEE Transactions on Pattern Analysis and Machine Intelligence 30 (5) (2008) 796–809
2008
Later among the works it cites.
M. Cheng, M. Ho, C. Huang, Gait analysis for human identification through manifold learning and hmm, Pattern Recognition 41 (8) (2008) 2541–2553
2008
Later among the works it cites.
J. A. Lee, M. Verleysen, Quality assessment of nonlinear dimensionality reduction based on k-ary neighborhoods, in: Journal of Machine Learning Research: Workshop and Conference proceedings, 2008, pp. 21–35
2008
Later among the works it cites.
Y. Cheon, D. Kim, Natural facial expression recognition using differential-aam and manifold learning, Pattern Recogn. 42 (7) (2009) 1340–1350
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2005
Cited alongside, same era.
S. Lafon, A. B. Lee, Diffusion maps and coarse-graining: A unified framework for dimensionality reduction, graph partitioning, and data set parameterization, IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (9) (2006) 1393–1403
2006
Cited alongside, same era.
K. Weinberger, L. Saul, Unsupervised learning of image manifolds by semidefinite programming, International Journal of Computer Vision 70 (1) (2006) 77–90
2006
Cited alongside, same era.
L. S. Chen, Local multidimensional scaling for nonlinear dimension reduction, graph layout and proximity analysis, Ph.D. thesis, University of Pennsylvania (2006)
2006
Cited alongside, same era.
J. Venna, S. Kaski, Local multidimensional scaling, Neural Networks 19 (6-7) (2006) 889–899
2006
Cited alongside, same era.
U. Akkucuk, J. D. Carroll, Paramap vs. isomap: A comparison of two nonlinear mapping algorithms, Journal of Classification 23 (2006) 221–254
2006
Cited alongside, same era.
V. d. Silva, J. B. Tenenbaum, Selecting landmark points for sparse manifold learning, in: Advances in Neural Information Processing Systems (NIPS), Vol. 18, 2006, pp. 1241–1248
2006
Cited alongside, same era.
L. Wang, D. Suter, Learning and matching of dynamic shape manifolds for human action recognition, IEEE Transactions on Image Processing 16 (6) (2007) 1646–1661
2007
Cited alongside, same era.
2009
Later among the works it cites.
Y. Goldberg, Y. Ritov, Local procrustes for manifold embedding: a measure of embedding quality and embedding algorithms, Machine Learning 77 (1) (2009) 1–25
2009
Later among the works it cites.
L. Chen, A. Buja, Local multidimensional scaling for nonlinear dimension reduction, graph drawing, and proximity analysis, Journal of the American Statitical Association 104 (485) (2009) 209–219
2009
Later among the works it cites.
J. Valencia-Aguirre, A. Álvarez Mesa, G. Daza-Santacoloma, G. Castellanos-Domínguez, Automatic choice of the number of nearest neighbors in locally linear embedding, in: CIARP ’09: Proceedings of the 14th Iberoamerican Conference on Pattern Recognition, 2009, pp. 77–84
2009
Later among the works it cites.
J. A. Lee, M. Verleysen, Quality assessment of dimensionality reduction: Rank-based criteria, Neurocomputing 72 (7-9) (2009) 1431–1443
2009
Later among the works it cites.
2009
Later among the works it cites.
H. Qiao, P. Zhang, B. Zhang, S. Zheng, Learning an intrinsic-variable preserving manifold for dynamic visual tracking, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 40 (3) (2010) 868–880
2010
Later among the works it cites.
G. Daza-Santacoloma, C. D. Acosta-Medina, G. Castellanos-Domínguez, Regularization parameter choice in locally linear embedding, Neurocomputing 73 (10-12) (2010) 1595–1605
2010
Later among the works it cites.
J. A. Lee, M. Verleysen, Scale-independent quality criteria for dimensionality reduction, Pattern Recognition Letters 31 (2010) 2248–2257
2010
Later among the works it cites.
P. Zhang, H. Qiao, B. Zhang, An improved local tangent space alignment method for manifold learning, Pattern Recognition Letters 32 (2) (2011) 181–190
2011
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