Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations

Abstract

Sketching is a randomized dimensionalityreduction method that aims to preserve relevant information in large-scale datasets. In this paper, we propose a novel extension known as Higher-order Count Sketch (HCS). We derive efficient (approximate) computation of various tensor operations such as tensor products and tensor contractions directly on the sketched data. HCS is the first sketch to fully exploit the multi-dimensional nature of higher-order tensors.

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