A New Truncation Strategy for the Higher-Order Singular Value Decomposition

By Nick Vannieuwenhoven, Raf Vandebril, Karl Meerbergen. 2012

304 citations recorded by the source corpus.

Subfields: Computational Mathematics, Computational Theory and Mathematics, Statistical and Nonlinear Physics

Citations in the corpus

• A literature survey of low‐rank tensor approximation techniques (2013, neutral): “Recent developments include Newton-type methods on manifolds [71, 131, 132, 229], a Jacobi algorithm for symmetric tensors [130], generalizations of Krylov subspace meth-ods [98,228], and modifications of the HOSVD [259].”
• Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions (2016, unknown): “For example, if Rn ! In, the truncated tensor Z Ð X ˆ1 U(1)T yields a smaller unfolding matrix Z(2) P RI2ˆR1 I3 ̈ ̈ ̈IN , so the multiplication Z(2)Z (2) can be faster in the next iterations, as illustrated in Algorithm 4 [10, 265].”