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Sketching for Linear Algebra III: Randomized Hadamard, Kernel Methods

Machine Learning seminar by Ken Clarkson, IBM Almaden

Hosted by Simons Institute for the Theory of Computing

Tuesday 09:30–10:30 Los Angeles (GMT-7)

Recording available

Berkeley, California, USA

Recording

Abstract

Building on the preceding linear-algebra tutorials, this lecture presents two further approaches to matrix sketching: leverage-score sampling and the Subsampled Randomized Hadamard Transform. It examines how these methods produce effective compressed matrix representations for a variety of applications.

Topics

leverage-score samplingSubsampled Randomized Hadamard Transformmatrix sketchingkernel methods

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