I. T. Joliffe. Principal Component Analysis. Springer-Verlag, New York, 1986.
W. S. Torgerson. Multidimensional Scaling, I: Theory and methods. Psychometrica, 17:401-419, 1952.
J. B. Tanenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319-2323, 2000.
S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323-2326, 2000.
M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15 (6): 1373-1396, 2003.
D. L. Donoho and C. E. Grimes. Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. Proceedings of the National Academy of Arts and Sciences, (100):55915596, 2003.
D. L. Donoho and C. E. Grimes. When does isomap recover natural parameterization of families of articulated images? Technical Report 2002-27, Stanford University 2002.
J. Wang, Z. Zhang and H. Zha, Adaptive manifold learning. NIPS, 2004.
J. Costa, A. Girotra and A. O. Hero, Estimating local intrinsic dimension with k-nearest neighbor graphs. In IEEE Workshop on Statistical Signal Processing (SSP), Bordeaux (2005).
E. Levina and P.J. Bickel, Maximum likelihood estimation of intrinsic dimension. In K.L. Saul, Y. Weiss and I. Bottou, Adances in Neural Information Processing Systems 17. MIT Press, Cambridge, MA (2005) 777-784.
D. M. De Maesschalck and R. D. Jouan-Rimbaud. The Mahalanobis distance. Chemometrics and Intelligent Laboratory Systems, 50:118.
