Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Kriging in Tensor Train data format   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.

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The categories of this paper are: eess
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The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
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The categories of this paper are: q-bio
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The categories of this paper are: plasm-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-ex