基于集成的流形学习可视化

[1] A global geometric framework for nonlinear dimensionality reduction. J. B. Tenenbaum,V. de Silva,J. C. Langford. Science . 2000

[2] Global versus local methods in nonlinear dimensionality reduction. V. D. Silva,J. B. Tenenbaum. In: Advances of Neural Information Proceeding Systems 15 . 2002

[3] Ensemble learning. T. G. Dietterich. In: The Handbook of Brain Theory and Neural Networks, 2nd Edition . 2002

[4] The isomap algorithm and topological stability. Balasubramanian M,Schwartz E L,Tenenbaum J B,et al. Science . 2002

[5] Statistical learning theory. V.N. Vapnik. . 1998

[6] Laplacian eigenmaps for dimensionality reduction and data representation. M. Belkin,P. Niyogi. Neural Computation . 2001

[7] Nonlinear dimensionality reduction by locally linear embedding. S. T. Roweis,K. S. Lawrance. Science . 2000

[1] A global geometric framework for nonlinear dimensionality reduction. J. B. Tenenbaum,V. de Silva,J. C. Langford. Science . 2000

[2] Global versus local methods in nonlinear dimensionality reduction. V. D. Silva,J. B. Tenenbaum. In: Advances of Neural Information Proceeding Systems 15 . 2002

[3] Ensemble learning. T. G. Dietterich. In: The Handbook of Brain Theory and Neural Networks, 2nd Edition . 2002

[4] The isomap algorithm and topological stability. Balasubramanian M,Schwartz E L,Tenenbaum J B,et al. Science . 2002

[5] Statistical learning theory. V.N. Vapnik. . 1998

[6] Laplacian eigenmaps for dimensionality reduction and data representation. M. Belkin,P. Niyogi. Neural Computation . 2001

[7] Nonlinear dimensionality reduction by locally linear embedding. S. T. Roweis,K. S. Lawrance. Science . 2000