About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
Information Processing Letters
Paper
A note on sparse least-squares regression
Abstract
We compute a sparse solution to the classical least-squares problem minx||Ax-b||2, where A is an arbitrary matrix. We describe a novel algorithm for this sparse least-squares problem. The algorithm operates as follows: first, it selects columns from A, and then solves a least-squares problem only with the selected columns. The column selection algorithm that we use is known to perform well for the well studied column subset selection problem. The contribution of this article is to show that it gives favorable results for sparse least-squares as well. Specifically, we prove that the solution vector obtained by our algorithm is close to the solution vector obtained via what is known as the "SVD-truncated regularization approach". © 2013 Elsevier B.V.