In recent years, the application of tensors has become more widespread in fields that involve data analytics and numerical computation. Due to the explosive growth of data, low-rank tensor decompositions have become a powerful tool to harness the notorious curse of dimensionality. The main forms of tensor decomposition include CP decomposition, Tucker decomposition, tensor train (TT) decomposition, etc. Each of the existing TT decomposition algorithms, including the TT-SVD and randomized TT-SVD, is successful in the field, but neither can both accurately and efficiently decompose large-scale sparse tensors. Based on previous research, this paper proposes a new quasi-optimal fast TT decomposition algorithm for large-scale sparse tensors with proven correctness and the upper bound of computational complexity derived. It can also efficiently produce sparse TT with no numerical error and slightly larger TT-ranks on demand. In numerical experiments, we verify that the proposed algorithm can decompose sparse tensors in a much faster speed than the TT-SVD, and have advantages on speed, precision and versatility over the randomized TT-SVD and TT-cross. And, with it we can realize large-scale sparse matrix TT decomposition that was previously unachievable, enabling the tensor decomposition based algorithms to be applied in more scenarios.
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- Parallel-vector rounding
- Sparse data
- Tensor train decomposition