How to Fake Multiply by a Gaussian Matrix


Michael Kapralov, Vamsi Potluru, David Woodruff ;
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2101-2110, 2016.


Have you ever wanted to multiply an n \times d matrix X, with n ≫d, on the left by an m \times n matrix \tilde G of i.i.d. Gaussian random variables, but could not afford to do it because it was too slow? In this work we propose a new randomized m \times n matrix T, for which one can compute T ⋅X in only O(nnz(X)) + \tilde O(m^1.5 ⋅d^3) time, for which the total variation distance between the distributions T ⋅X and \tilde G ⋅X is as small as desired, i.e., less than any positive constant. Here nnz(X) denotes the number of non-zero entries of X. Assuming nnz(X) ≫m^1.5 ⋅d^3, this is a significant savings over the naïve O(nnz(X) m) time to compute \tilde G ⋅X. Moreover, since the total variation distance is small, we can provably use T ⋅X in place of \tilde G ⋅X in any application and have the same guarantees as if we were using \tilde G ⋅X, up to a small positive constant in error probability. We apply this transform to nonnegative matrix factorization (NMF) and support vector machines (SVM).

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