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• W3094175033 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 variance, and we show that the scaling Rademacher variable is an optimal choice for generating the corresponding TT-cores. Detailed numerical experiments with synthetic datasets and the MNIST dataset are conducted to demonstrate the efficiency of TTRP." @default.
• W3094175033 created "2020-10-29" @default.
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• W3094175033 date "2023-01-01" @default.
• W3094175033 modified "2023-09-26" @default.
• W3094175033 title "Tensor Train Random Projection" @default.
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• W3094175033 doi "https://doi.org/10.32604/cmes.2022.021636" @default.
• W3094175033 hasPublicationYear "2023" @default.
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