Weighted Krylov-Levenberg-Marquardt method for canonical polyadic tensor decomposition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F20%3A00523836" target="_blank" >RIV/67985556:_____/20:00523836 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICASSP40776.2020.9054312" target="_blank" >http://dx.doi.org/10.1109/ICASSP40776.2020.9054312</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSP40776.2020.9054312" target="_blank" >10.1109/ICASSP40776.2020.9054312</a>
Alternative languages
Result language
angličtina
Original language name
Weighted Krylov-Levenberg-Marquardt method for canonical polyadic tensor decomposition
Original language description
Weighted canonical polyadic (CP) tensor decomposition appears in a wide range of applications. A typical situation where the weighted decomposition is needed is when some tensor elements are unknown, and the task is to fill in the missing elements under the assumption that the tensor admits a low-rank model. The traditional methods for large-scale decomposition tasks are based on alternating least-squares methods or gradient methods. Second-order methods might have significantly better convergence, but so far they were used only on small tensors. The proposed Krylov-Levenberg-Marquardt method enables to do second-order-based iterations even in large-scale decomposition problems, with or without weights. We show in simulations that the proposed technique can outperform existing state-of-the-art algorithms in some scenarios.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/GA17-00902S" target="_blank" >GA17-00902S: Advanded Joint Blind Source Separation Methods</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2020
ISBN
978-1-5090-6631-5
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
3917-3921
Publisher name
IEEE
Place of publication
Piscataway
Event location
Barcelona
Event date
May 4, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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