Random projection, margins, kernels, and feature-selection

author: Avrim Blum, School of Computer Science, Carnegie Mellon University
published: Feb. 25, 2007,   recorded: February 2005,   views: 7667


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Random projection is a simple technique that can often provide insight into questions such as "why is it good to have a large margin?" or "what are kernels really doing and how are they similar to feature selection?" In this talk I will describe some simple learning algorithms using random projection. I will then discuss how, given a kernel as a black-box function, we can use various forms of random projection to extract an explicit small feature space that captures much of the power of the given kernel function.

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Reviews and comments:

Comment1 ml student, October 30, 2009 at 2:54 a.m.:


Comment2 Tom Diethe, April 1, 2010 at 3:35 p.m.:

Real shame it stops in the middle - interesting stuff

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