Convex Sparse Methods for Feature Hierarchies
published: Aug. 26, 2009, recorded: June 2009, views: 4450
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Sparse methods usually deal with the selection of a few elements from a large collection of pre-computed features. While theoretical results suggest that techniques based on the L1-norm can deal with exponentially many irrelevant features, current algorithms cannot handle more than millions of variables. In this talk, I will show how structured norms can deal in polynomial time with exponentially many features that are organized in a directed acyclic graph.
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