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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"><html xmlns="http://www.w3.org/TR/REC-html40"> <head> <title>Data Processing with Manifold Methods</title> <meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1"> <link href="styles.css" rel="stylesheet" type="text/css"> </head> <body bgcolor="#ffffff" lang="EN"><p><b>Data Processing Using Manifold Methods - References</b></p><p><strong>Surface parameterization using differential calculus methods:</strong></p><ul> <li><em>Surface Parameterization: a Tutorial and Survey</em>,<br> M. S. Floater and K. Hormann, in Advances in Multiresolution for Geometric Modelling, Springer-Verlag, Heidelberg, 2005, 157-186. 536K</li></ul><p><strong>Fast Marching methods (including on surfaces):</strong></p><ul> <li><em>Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision and Materials Science</em><br> J.A. Sethian, Cambridge University Press, 1999.</li></ul><p> <strong>Classical dimension reduction methods:</strong></p><ul> <li><em>A Survey of Dimension Reduction Techniques</em><br> Imola Fodor, 2002</li> <li><em>Support Vector Machines and Kernel Algorithms</em> - Schölkopf, B. and A. J. Smola<br> Encyclopedia of Biostatistics 8, 5328-5335. (Eds.) Armitage, P., T. Colton, John Wiley & Sons, NY USA (2005)</li></ul><p> <strong>Laplacian kernels:</strong></p><ul> <li><em>Diffusion Kernels </em><br> R. Kondor and J.-P. Vert. In "Kernel Methods in Computational Biology" ed. B. Scholkopf, K. Tsuda and J.-P. Vert. The MIT Press 2004.</li> <li><em>The Geometric Basis of Semi-supervised Learning</em><br> V. Sindhwani, M. Belkin, P. Niyogi. 2006, in print.</li> <li><em>Discrete Regularization. Semi-Supervised Learning</em>,<br> Zhou, D. and B. Schölkopf: 237-249. (Eds.) Chapelle, O., B. Schölkopf and A. Zien, MIT Press, Cambridge, MA, USA (2006)</li></ul><p> <strong>Manifold learning:</strong></p><ul> <li><a href="http://www.cse.msu.edu/%7Elawhiu/manifold/">Manifold learning ressources</a></li> <li><em> Global versus local methods in nonlinear dimensionality reduction,</em><br> V. de Silva and J. B. Tenenbaum. Neural Information Processing Systems 15 (NIPS'2002), pp. 705-712, 2003.</li> <li> <em>Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds.</em><br> L. K. Saul and S. T. Roweis. Journal of Machine Learning Research, v4, pp. 119-155, 2003. </li></ul><p> Copyright ⌨️ 快捷键说明
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