A New Sampling Technique for Tensors

In this paper we propose new techniques to sample arbitrary third-order tensors, with an objective of speeding up tensor algorithms that have recently gained popularity in machine learning. Our main contribution is a new way to select, in a biased random way, only O(n/ǫ) of the possible n elements while still achieving each of the three goals: (a) tensor sparsification: for a tensor that has to be formed from arbitrary samples, compute very few elements to get a good spectral approximation, and for arbitrary orthogonal tensors (b) tensor completion: recover an exactly low-rank tensor from a small number of samples via alternating least squares, or (c) tensor factorization: approximating factors of a low-rank tensor corrupted by noise. Our sampling can be used along with existing tensor-based algorithms to speed them up, removing the computational bottleneck in these methods.