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Dimensionality Reduction: PCA and Gauss. Proc. Factor Analysis, by Frederic Simard

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Published on Jul 5, 2013

In this talk, you will learn the basics of dimensionality reduction. The first algorithm that is presented is the principal component analysis (PCA) which is based on explaining the variance in the data set. You will learn how to select a subset of dimensions while maintaining the most information about your data, as to, for example, make a classifier. A quick presentation of the Gaussian Process Factor Analysis follows. This algorithm extract trajectories of a system state in lower dimension space.

Speaker: Frederic Simard
Contact: frederic.simard@mail.mcgill.ca
Website: www.atomsproducts.com

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