Sparsity-based regularization

Lorenzo Rosasco


We introduce the problem of feature selection in supervised learning, focusing on sparsity based regularization techniques.

Class Reference Matterial

Chapter 6 - Sparsity, Low Rank and All That,
L. Rosasco, T. Poggio, A Regularization Tour of Machine Learning, MIT-9.520 Lectures Notes (book draft), 2015.

Note: The course notes, in the form of the book draft circulated is the reference material for this class.
Related and older material for this class can be accessed through the link for the previous year offerings of the course.

Further Reading

C. De Mol, E. De Vito and L. Rosasco, Elastic-Net Regularization in Learning Theory, Journal of Complexity 25(2), April 2009 (and references therein).