Complex-to-real sketches for tensor products with applications to the polynomial kernel

Randomized sketches of a tensor product of p vectors follow a tradeoff between statistical efficiency and computational acceleration. Commonly used approaches avoid computing the high-dimensional tensor product explicitly, resulting in a suboptimal dependence of O(3p ) in the embedding dimension. We propose a simple Complex-to-Real (CtR) modification of wellknown sketches that replaces real random projections by complex ones, incurring a lower O(2p ) factor in the embedding dimension. The output of our sketches is real-valued, which renders their downstream use straightforward. In particular, we apply our sketches to p-fold self-tensored inputs corresponding to the feature maps of the polynomial kernel. We show that our method achieves state-of-the-art performance in terms of accuracy and speed compared to other randomized approximations from the literature.