A Unified Framework for Dimensionality Reduction
暂无分享，去创建一个
[1] Bernhard Schölkopf,et al. Kernel Principal Component Analysis , 1997, ICANN.
[2] Mikhail Belkin,et al. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.
[3] David L. Donoho,et al. Aide-Memoire . High-Dimensional Data Analysis : The Curses and Blessings of Dimensionality , 2000 .
[4] Felipe Cucker,et al. Learning Theory: An Approximation Theory Viewpoint: Index , 2007 .
[5] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[6] Yuan Yao,et al. Mercer's Theorem, Feature Maps, and Smoothing , 2006, COLT.
[7] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[8] Bernhard Schölkopf,et al. A kernel view of the dimensionality reduction of manifolds , 2004, ICML.
[9] Kilian Q. Weinberger,et al. Learning a kernel matrix for nonlinear dimensionality reduction , 2004, ICML.
[10] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[11] A. Atiya,et al. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.
[12] Ian T. Jolliffe,et al. Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.
[13] Yiming Yang,et al. A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.