Statistics – Machine Learning
Scientific paper
2012-01-16
Statistics
Machine Learning
Scientific paper
This paper develops a general theoretical framework to analyze structured sparse recovery problems using the notation of dual certificate. Although certain aspects of the dual certificate idea have already been used in some previous work, due to the lack of a general and coherent theory, the analysis has so far only been carried out in limited scopes for specific problems. In this context the current paper makes two contributions. First, we introduce a general definition of dual certificate, which we then use to develop a unified theory of sparse recovery analysis for convex programming. Second, we present a class of structured sparsity regularization called structured Lasso for which calculations can be readily performed under our theoretical framework. This new theory includes many seemingly loosely related previous work as special cases; it also implies new results that improve existing ones even for standard formulations such as L1 regularization.
Zhang Cun-Hui
Zhang Tong
No associations
LandOfFree
A General Framework of Dual Certificate Analysis for Structured Sparse Recovery Problems 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 A General Framework of Dual Certificate Analysis for Structured Sparse Recovery Problems, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A General Framework of Dual Certificate Analysis for Structured Sparse Recovery Problems will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-410435