Structured Sparsity Regularization

Description

We describe a set of approaches for regularization with richer prior information than sparsity, refered to as structured sparsity regularization.

Class Reference Material

L. Rosasco, T. Poggio, Machine Learning: a Regularization Approach, MIT-9.520 Lectures Notes, Manuscript, Dec. 2017

Chapter 6 - Sparsity, Low Rank and All That


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

Further Reading

• S. Mosci, L. Rosasco, M. Santoro, A. Verri, and S. Villa, Solving structured sparsity regularization with proximal methods, Machine Learning and Knowledge Discovery in Databases, pages 418-433, Springer, 2010.
• F. Bach, R. Jenatton, J. Mairal, and G. Obozinski, Optimization with sparsity-inducing penalties, Foundations and Trends in Machine Learning, 4(1), 1-106, 2012.
• P. L. Combettes and J.-C. Pesquet, Proximal Splitting Methods in Signal Processing, In Fixed-point algorithms for inverse problems in science and engineering, 185–212, Springer, 2011.