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Sparse Methods for Machine Learning: Theory and Algorithms

Sparse Methods for Machine Learning: Theory and Algorithms

This video was recorded at 23rd Annual Conference on Neural Information Processing Systems (NIPS), Vancouver 2009. Regularization by the L1-norm has attracted a lot of interest in recent years in statistics, machine learning and signal processing. In the context of least-square linear regression, the problem is usually referred to as the Lasso or basis pursuit. Much of the early effort has been dedicated to algorithms to solve the optimization problem efficiently, either through first-order methods, or through homotopy methods that leads to the entire regularization path (i.e., the set of solutions for all values of the regularization parameters) at the cost of a single matrix inversion. A well-known property of the regularization by the L1-norm is the sparsity of the solutions, i.e., it... Show More


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