1. Jeffrey M. DiCarlo and Brian A. Wandell. Spectral estimation theory: beyond linear but before Bayesian. J. Opt. Soc. Am. A., 20(7):1261-1270, 2003.
   Keywords: dimension reduction. [Abstract] [Comments] [bibtex-entry]



2. S. T. Roweis and L. K. Saul. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 290:2323-2326, December 2000.
   Keywords: geometry, dimension reduction. [Abstract] [bibtex-entry]



3. J. B. Tenenbaum, V. de Silva, and J. C. Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 290:2319-2323, 2000.
   Keywords: geometry, dimension reduction. [Abstract] [bibtex-entry]



4. J. B. Tenenbaum. Mapping a Manifold of Perceptual Observations. NIPS-10, 1998.
   Keywords: geometry, dimension reduction. [Abstract] [Comments] [bibtex-entry]



5. Shimon Edelman and Nathan Intrator. Learning as extraction of low-dimensional representations. november 1997.
   Keywords: dimension reduction, learning. [Abstract] [bibtex-entry]

1. John Aldo Lee, Amaury Lendasse, Nicolas Donckers, and Michel Verleysen. A robust nonlinear projection method. In European Symposium on Artificial Neural Networks, pages 13-20, 2000.
   Keywords: ingeneering, dimension reduction. [Abstract] [bibtex-entry]



2. Pierre Demartines and Jeanny Hérault. CCA: Curvilinear Component Analysis. In 15th workshop GRETSI, 1995.
   Keywords: ingeneering, dimension reduction. [bibtex-entry]

All the ressources below are referenced through direct links, for easier use as a library of introductory and comprehensive papers on various subjects. If a paper of yours is on this list and you do not want your homepage to be bypassed, just send me an email.

Comments and ratings are for personnal use, they do not claim to reflect academic value. It is only an indication of their relevance with respect to my own interests and the success they happened to have in the scientific community.

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