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Nonlinear Dimensionality Reduction

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If you have a question about this talk, please contact Zoubin Ghahramani.

Frederik and Arik will discuss some methods (Kernel PCA , LLE and Isomap) for finding nonlinear structure in data.

Relevant readings include:

Kernel PCA :



Optional additional readings:

Semidefinite Embedding / Maximum Variance Unfolding:×1/

Hessian LLE :

This meeting will be held at the usual place and time, on Thursday from 13:00 to 15:00, on the 5th floor of the Engineering Department, in room 505.

This talk is part of the Machine Learning Reading Group @ CUED series.

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