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Krylov-Levenberg-Marquardt Algorithm for Structured Tucker Tensor Decompositions

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    0541614 - ÚTIA 2022 RIV US eng J - Journal Article
    Tichavský, Petr - Phan, A. H. - Cichocki, A.
    Krylov-Levenberg-Marquardt Algorithm for Structured Tucker Tensor Decompositions.
    IEEE Journal on Selected Topics in Signal Processing. Roč. 15, č. 3 (2021), s. 550-559. ISSN 1932-4553. E-ISSN 1941-0484
    Grant - others:GA ČR(CZ) GA20-17720S
    Institutional support: RVO:67985556
    Keywords : canonical polyadic tensor decomposition * parallel factor analysis * tensor chain * sensitivity
    OECD category: Electrical and electronic engineering
    Impact factor: 7.695, year: 2021
    Method of publishing: Limited access
    http://library.utia.cas.cz/separaty/2021/SI/tichavsky-0541614.pdf https://ieeexplore.ieee.org/document/9354901

    Structured Tucker tensor decomposition models complete or incomplete multiway data sets (tensors), where the core tensor and the factor matrices can obey different constraints. The model includes block-term decomposition or canonical polyadic decomposition as special cases. We propose a very flexible optimization method for the structured Tucker decomposition problem, based on the second-order Levenberg-Marquardt optimization, using an approximation of the Hessian matrix by the Krylov subspace method. An algorithm with limited sensitivity of the decomposition is included. The proposed algorithm is shown to perform well in comparison to existing tensor decomposition methods.

    Permanent Link: http://hdl.handle.net/11104/0319267

     
     
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