Krylov-Levenberg-Marquardt Algorithm for Structured Tucker Tensor Decompositions

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00541614" target="_blank" >RIV/67985556:_____/21:00541614 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9354901" target="_blank" >https://ieeexplore.ieee.org/document/9354901</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/JSTSP.2021.3059521" target="_blank" >10.1109/JSTSP.2021.3059521</a>

Result language
angličtina
Original language name
Krylov-Levenberg-Marquardt Algorithm for Structured Tucker Tensor Decompositions
Original language description
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.n
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20201 - Electrical and electronic engineering

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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