On Truncated-SVD-like Sparse Solutions to Least-Squares Problems of Arbitrary Dimensions
arXiv:1201.0073
Abstract
We describe two algorithms for computing a sparse solution to a least-squares problem where the coefficient matrix can have arbitrary dimensions. We show that the solution vector obtained by our algorithms is close to the solution vector obtained via the truncated SVD approach.
This paper has been withdrawn by the author. This article has been replaced by another submission: arXiv:1312.7499