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Convex Sparse Methods for Feature Hierarchies

Convex Sparse Methods for Feature Hierarchies

This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. 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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