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Krylov-Levenberg-Marquardt Algorithm for Structured Tucker Tensor Decompositions
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SYSNO ASEP 0541614 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Krylov-Levenberg-Marquardt Algorithm for Structured Tucker Tensor Decompositions Author(s) Tichavský, Petr (UTIA-B) RID, ORCID
Phan, A. H. (RU)
Cichocki, A. (RU)Number of authors 3 Source Title IEEE Journal on Selected Topics in Signal Processing. - : Institute of Electrical and Electronics Engineers - ISSN 1932-4553
Roč. 15, č. 3 (2021), s. 550-559Number of pages 10 s. Publication form Print - P Language eng - English Country US - United States Keywords canonical polyadic tensor decomposition ; parallel factor analysis ; tensor chain ; sensitivity Subject RIV BB - Applied Statistics, Operational Research OECD category Electrical and electronic engineering Method of publishing Limited access Institutional support UTIA-B - RVO:67985556 UT WOS 000637533400010 EID SCOPUS 85100948273 DOI 10.1109/JSTSP.2021.3059521 Annotation 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.
Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2022 Electronic address https://ieeexplore.ieee.org/document/9354901
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