Subspace Embedding and Linear Regression with Orlicz Norm

We consider a generalization of the classic linear regression problem to the case when the loss is an Orlicz norm. An Orlicz norm is parameterized by a non-negative convex function G: R+ - > R+ with G(0) = 0: the Orlicz norm of a n-dimensional vector x is defined as |x|G = inf{ alpha > 0 | sum{i = 1}^n G( |xi| / alpha ) < = 1 }. We consider the cases where the function G grows subquadratically. Our main result is based on a new oblivious embedding which embeds the column space of a given nxd matrix A with Orlicz norm into a lower dimensional space with L2 norm. Specifically, we show how to efficiently find an mxn embedding matrix S (m < n), such that for every d-dimensional vector x, we have Omega(1/(d log n)) |Ax|G < = |SAx|2 < = O(d^2 log n) |Ax|G. By applying this subspace embedding technique, we show an approximation algorithm for the regression problem minx |Ax-b|G, up to a O( d log^2 n ) factor. As a further application of our techniques, we show how to also use them to improve on the algorithm for the Lp low rank matrix approximation problem for 1 < = p < 2.