Learning with Structured Sparsity

author: Junzhou Huang, Department of Computer Science, Rutgers, The State University of New Jersey
published: Aug. 26, 2009,   recorded: June 2009,   views: 639
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Description

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.

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