Practical Leverage-Based Sampling for Low-Rank Tensor Decomposition
Practical Leverage-Based Sampling for Low-Rank Tensor Decomposition
The low-rank canonical polyadic tensor decomposition is useful in data analysis and can be computed by solving a sequence of overdetermined least squares subproblems. Motivated by consideration of sparse tensors, we propose sketching each subproblem using leverage scores to select a subset of the rows, with probabilistic guarantees on the …