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Random projection, margins, kernels, and feature-selection
This video was recorded at Workshop on Subspace, Latent Structure and Feature Selection Techniques: Statistical and Optimisation Perspectives, Bohinj 2005. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
