TY - GEN

T1 - Learning with structured sparsity

AU - Huang, Junzhou

AU - Zhang, Tong

AU - Metaxas, Dimitris

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=71149112556&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=71149112556&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:71149112556

SN - 9781605585161

T3 - Proceedings of the 26th International Conference On Machine Learning, ICML 2009

SP - 417

EP - 424

BT - Proceedings of the 26th International Conference On Machine Learning, ICML 2009

T2 - 26th International Conference On Machine Learning, ICML 2009

Y2 - 14 June 2009 through 18 June 2009

ER -