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Fast Random Feature Expansions for Nonlinear Regression

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Published on Jan 18, 2014

0:01 The theoretical aspects of the random projection method
49:00 The practical part of the tutorial starts.
1:08:53 Explanation of the Fastfood algorithm

A tutorial given to the Cambridge Machine Learning Group by
David Lopez-Paz
http://people.tuebingen.mpg.de/dlopez/
and
David Duvenaud
http://mlg.eng.cam.ac.uk/duvenaud/

The first half derives the Johnson-Lindenstrauss Lemma, which states that one may randomly project a collection of data points into a lower dimensional space while preserving pairwise point distances.

The second half discusses recent developments that have gone even further: non-linear randomised projections can be used to approximate kernel machines and scale them to datasets with millions of features and samples.

Slides available at:
http://mlg.eng.cam.ac.uk/duvenaud/tal...

Q.V. Le, T. Sarlos, A.J. Smola. (2013)
Fastfood: Approximating Kernel Expansions in Loglinear Time
http://cs.stanford.edu/~quocle/LeSarl...

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