Yani Feng1, Kejun Tang2, Lianxing He3, Pingqiang Zhou1, Qifeng Liao1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1195-1218, 2023, DOI:10.32604/cmes.2022.021636
- 31 August 2022
Abstract This work proposes a Tensor Train Random Projection (TTRP) method for dimension reduction, where pairwise distances can be approximately preserved. Our TTRP is systematically constructed through a Tensor Train (TT) representation with TT-ranks equal to one. Based on the tensor train format, this random projection method can speed up the dimension reduction procedure for high-dimensional datasets and requires fewer storage costs with little loss in accuracy, compared with existing methods. We provide a theoretical analysis of the bias and the variance of TTRP, which shows that this approach is an expected isometric projection with bounded More >
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