Learning with structured sparsity

Authors:
Junzhou Huang;Tong Zhang;Dimitris Metaxas
Affiliations:
Rutgers University, Piscataway, NJ;Rutgers University, Piscataway, NJ;Rutgers University, Piscataway, NJ
Venue:
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Year:
2009

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Abstract

This paper investigates a new learning formulation called structured sparsity, which is a natural extension of the standard sparsity concept in statistical learning and compressive sensing. By allowing arbitrary structures on the feature set, this concept generalizes the group sparsity idea. A general theory is developed for learning with structured sparsity, based on the notion of coding complexity associated with the structure. Moreover, a structured greedy algorithm is proposed to efficiently solve the structured sparsity problem. Experiments demonstrate the advantage of structured sparsity over standard sparsity.