On Truncated-SVD-like Sparse Solutions to Least-Squares Problems of Arbitrary Dimensions
arXiv:1201.00731 citationsh-index: 23
AI Analysis
This work provides practical algorithms for sparse least-squares problems, but the improvement over existing methods is incremental.
The paper presents two algorithms for finding sparse solutions to least-squares problems with arbitrary-sized coefficient matrices, demonstrating that the resulting solution approximates the truncated SVD solution.
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.