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References
T. Mitchell, Machine Learning (McGraw-Hill, New York, 1997)
I. Jolliffe, Principal Component Analysis (Springer, New York, 1986)
T. Cox, M. Cox, Multidimensional Scaling (Chapman & Hall, London, 1994)
S. Roweis, L. Saul, Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)
J. Tenenbaum, V. de Silva, J. Langford, A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)
M. Brand, Charting a manifold, in Advances in Neural Information Processing Systems, vol. 15 (MIT, Cambridge, 2003), pp. 961–968
M. Belkin, P. Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)
P. Mordohai, G. Medioni, Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning (Morgan & Claypool, San Rafael, 2006)
J. Bruske, G. Sommer, Intrinsic dimensionality estimation with optimally topology preserving maps. IEEE Trans. Pattern Anal. Mach. Intell. 20(5), 572–575 (1998)
B. Kégl, Intrinsic dimension estimation using packing numbers, in Advances in Neural Information Processing Systems, vol. 15 (MIT, Cambridge, 2003), pp. 681–688
M. Raginsky, S. Lazebnik, Estimation of intrinsic dimensionality using high-rate vector quantization, in Advances in Neural Information Processing Systems, vol. 18 (MIT, Cambridge, 2006), pp. 1105–1112
J. Costa, A. Hero, Geodesic entropic graphs for dimension and entropy estimation in manifold learning. IEEE Trans. Signal Process. 52(8), 2210–2221 (2004)
P. Mordohai, G. Medioni, Unsupervised dimensionality estimation and manifold learning in high-dimensional spaces by tensor voting, in International Joint Conference on Artificial Intelligence, Pittsburgh, 2005
B. Schölkopf, A. Smola, K.R. Müller, Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5), 1299–1319 (1998)
D. Donoho, C. Grimes, Hessian eigenmaps: new tools for nonlinear dimensionality reduction, in Proceedings of National Academy of Science, 2003, pp. 5591–5596
K.Q. Weinberger, L.K. Saul, Unsupervised learning of image manifolds by semidefinite programming. Int. J. Comput. Vis. 70(1), 77–90 (2006)
S. Prince, J. Elder, Creating invariance to ‘nuisance parameters’ in face recognition, in International Conference on Computer Vision and Pattern Recognition, II, San Diego, 2005, pp. 446–453
W.K. Liao, G. Medioni, 3D face tracking and expression inference from a 2D sequence using manifold learning, in International Conference on Computer Vision and Pattern Recognition, Anchorage, 2008
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Mordohai, P., Medioni, G. (2015). Manifold Learning. In: Li, S.Z., Jain, A.K. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7488-4_301
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