Learning with Structured Sparsity

Details Learning with Structured Sparsity Learning with Structured Sparsity

2009-03-17

arxiv.org/abs/0903.3002v2

Statistics

Methodology

Scientific paper

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 that has become popular in recent years. A general theory is developed for learning with structured sparsity, based on the notion of coding complexity associated with the structure. It is shown that if the coding complexity of the target signal is small, then one can achieve improved performance by using coding complexity regularization methods, which generalize the standard sparse regularization. Moreover, a structured greedy algorithm is proposed to efficiently solve the structured sparsity problem. It is shown that the greedy algorithm approximately solves the coding complexity optimization problem under appropriate conditions. Experiments are included to demonstrate the advantage of structured sparsity over standard sparsity on some real applications.

Affiliated with

Also associated with

No associations

Rating

Learning with Structured Sparsity does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Learning with Structured Sparsity, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Learning with Structured Sparsity will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-491792