Before revision: We study " learning-based" sketching approaches for diverse tasks in numerical linear algebra: least-squares regression, \ell_p regression, Huber regression, low-rank approximation (LRA), and k-means clustering.
After revision: A sketching algorithm is a way to solve an optimization problem approximately and in a fraction of the usual time. We consider classical sketching algorithms which first compress data by multiplication with a random " learning-based" sketching approaches for diverse tasks in numerical linear algebra: least-squares regression, \ell_p regression, Huber regression, low-rank approximation (LRA), and k-means clustering.