Sparse recovery conditions for Orthogonal Least Squares

Details Sparse recovery conditions for Orthogonal Least Squares Sparse recovery conditions for Orthogonal Least Squares

2011-11-02

arxiv.org/abs/1111.0522v1

Statistics

Computation

30 pages

Scientific paper

We extend Tropp's analysis of Orthogonal Matching Pursuit (OMP) using the Exact Recovery Condition (ERC) to a first exact recovery analysis of Orthogonal Least Squares (OLS). We show that when ERC is met, OLS is guaranteed to exactly recover the unknown support. Moreover, we provide a closer look at the analysis of both OMP and OLS when ERC is not fulfilled. We show that there exist dictionaries for which some subsets are never recovered with OMP. This phenomenon, which also appears with $\ell_1$ minimization, does not occur for OLS. Finally, numerical experiments based on our theoretical analysis show that none of the considered algorithms is uniformly better than the other.

Affiliated with

Also associated with

No associations

Rating

Sparse recovery conditions for Orthogonal Least Squares 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 Sparse recovery conditions for Orthogonal Least Squares, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Sparse recovery conditions for Orthogonal Least Squares will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-329281