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• W2938565099 abstract "Combination of low-tensor rank techniques and the Fast Fourier transform (FFT) based methods had turned out to be prominent in accelerating various statistical operations such as Kriging, computing conditional covariance, geostatistical optimal design, and others. However, the approximation of a full tensor by its low-rank format can be computationally formidable. In this work, we incorporate the robust Tensor Train (TT) approximation of covariance matrices and the efficient TT-Cross algorithm into the FFT-based Kriging. It is shown that here the computational complexity of Kriging is reduced to $mathcal{O}(d r^3 n)$, where $n$ is the mode size of the estimation grid, $d$ is the number of variables (the dimension), and $r$ is the rank of the TT approximation of the covariance matrix. For many popular covariance functions the TT rank $r$ remains stable for increasing $n$ and $d$. The advantages of this approach against those using plain FFT are demonstrated in synthetic and real data examples." @default.
• W2938565099 created "2019-04-25" @default.
• W2938565099 creator A5018717402 @default.
• W2938565099 creator A5069489614 @default.
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• W2938565099 date "2019-04-21" @default.
• W2938565099 modified "2023-09-24" @default.
• W2938565099 title "Kriging in Tensor Train data format" @default.
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• W2938565099 doi "https://doi.org/10.48550/arxiv.1904.09668" @default.
• W2938565099 hasPublicationYear "2019" @default.
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