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. Sketching methods are used to quickly and approximately compute properties of large matrices. Linear maps called " sketches " are applied to compress data, and these concise representations are used to compute the desired properties.
After revision: We study " sketch matrix". Our work improves and extends the " sketches " are applied to compress data, and these concise representations are used to compute the desired properties.