A randomized algorithm for a tensor-based generalization of the singular value decomposition

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A literature survey of low‐rank tensor approximation techniques (2013, neutral): “These ideas have been extended to low-rank tensor decompositions, for the Tucker decomposition in [70,89,247] and for the TT decomposition in [219].”
Relative-Error CUR Matrix Decompositions (2007, background): “Sampling probabilities of the form (20) should be contrasted with sampling probabilities that depend on the Euclidean norms of the columns or rows of A and that have received much attention recently [33, 34, 21, 22, 23, 26].”
MACH: Fast Randomized Tensor Decompositions (2009, methodology): “To speed up tensor decompositions, randomized algorithms [12, 29] have appeared in the recent years.”
Tensor Decompositions and Applications (2009, background): “Mahoney, Maggioni, and Drineas [161] extend the matrix CUR decomposition to tensors; others have done related work using sampling methods for tensor approximation [74, 54].”