We provide a deterministic construction of the sparse JohnsonLindenstrauss transform of Kane & Nelson (J.ACM 2014) which runs, under a mild restriction, in the time necessary to apply the sparse embedding matrix to the input vectors. Specifically, given a set of n vectors in Rd and target error ϵ, we give a deterministic algorithm to compute a f1; 0; 1g embedding matrix of rank O((ln n)= ϵ 2) with O((ln n)/ϵ) entries per column which preserves the norms of the vectors to within 1ϵ. If NNZ, the number of non-zero entries in the input set of vectors, is (d2), our algorithm runs in time O(NNZ ln n/ϵ). One ingredient in our construction is an extremely simple proof of the Hanson-Wright inequality for subgaussian random variables, which is more amenable to derandomization. As an interesting byproduct, we are able to derive the essentially optimal form of the inequality in terms of its functional dependence on the parameters.

ACM-SIAM Symposium on Discrete Algorithms
Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

Dadush, D.N, Guzman Paredes, C.A, & Olver, N.K. (2018). Fast, deterministic and sparse dimensionality reduction. In Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 1330–1344). doi:10.1137/1.9781611975031.87