Tensor decomposition can reduce to rank-one approximations

Abstract : The Canonical Polyadic (CP) decomposition of a tensor is difficult to compute. Even algorithms computing the best rank-one approximation are not entirely satisfactory. And deflation approaches (successive rank-1 tensor approximations) do not work for tensors. However, there are cases where successive rank-1 matrix approximations can help in computing the CP decomposition. This is what we investigate in this talk. In particular, we analyze the cases where loading matrices are banded and structured, e.g. Toeplitz or Hankel.
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Communication dans un congrès
26th GAMM on Tensor Approximations and High-Dimensional Problems, Feb 2010, Leipzig, Germany
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https://hal.archives-ouvertes.fr/hal-00985606
Contributeur : Pierre Comon <>
Soumis le : mercredi 30 avril 2014 - 10:25:35
Dernière modification le : mercredi 30 avril 2014 - 10:25:35

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  • HAL Id : hal-00985606, version 1

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Pierre Comon. Tensor decomposition can reduce to rank-one approximations. 26th GAMM on Tensor Approximations and High-Dimensional Problems, Feb 2010, Leipzig, Germany. 〈hal-00985606〉

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