Subspace Embeddings and \ell_p-Regression Using Exponential Random Variables


David Woodruff, Qin Zhang ;
Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:546-567, 2013.


Oblivious low-distortion subspace embeddings are a crucial building block for numerical linear algebra problems. We show for any real p, 1 ≤p < ∞, given a matrix M ∈\mathbbR^n \times d with n ≫d, with constant probability we can choose a matrix \Pi with \max(1, n^1-2/p) \textpoly(d) rows and n columns so that simultaneously for all x ∈\mathbbR^d, \|Mx\|_p ≤\|\Pi Mx\|_∞ ≤\textpoly(d) \|Mx\|_p. Importantly, \Pi M can be computed in the optimal O(\textnnz(M)) time, where \textnnz(M) is the number of non-zero entries of M. This generalizes all previous oblivious subspace embeddings which required p ∈[1,2] due to their use of p-stable random variables. Using our new matrices \Pi, we also improve the best known distortion of oblivious subspace embeddings of \ell_1 into \ell_1 with \tildeO(d) target dimension in O(\textnnz(M)) time from \tildeO(d^3) to \tildeO(d^2), which can further be improved to \tildeO(d^3/2) \log^1/2 n if d = Ω(\log n), answering a question of Meng and Mahoney (STOC, 2013). We apply our results to \ell_p-regression, obtaining a (1+ε)-approximation in O(\textnnz(M)\log n) + \textpoly(d/ε) time, improving the best known \textpoly(d/ε) factors for every p ∈[1, ∞) ∖{2}. If one is just interested in a \textpoly(d) rather than a (1+ε)-approximation to \ell_p-regression, a corollary of our results is that for all p ∈[1, ∞) we can solve the \ell_p-regression problem without using general convex programming, that is, since our subspace embeds into \ell_∞ it suffices to solve a linear programming problem. Finally, we give the first protocols for the distributed \ell_p-regression problem for every p ≥1 which are nearly optimal in communication and computation.

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