Sparse methods for machine learning: Theory and algorithms
published: Nov. 16, 2010, recorded: September 2010, views: 949
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Sparse methods such as 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. The objective of the tutorial is to give a unified overview of the recent contributions of sparse convex methods to machine learning, both in terms of theory and algorithms. The course will be divided in three parts: in the first part, the focus will be on the regular L1-norm and variable selection, introducing key algorithms and key theoretical results. Then, several more structured machine learning problems will be discussed, on vectors (second part) and matrices (third part), such as multi-task learning, sparse principal component analysis, multiple kernel learning and sparse coding.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !