Tensor Chain Decomposition and Function Interpolation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F23%3A00574036" target="_blank" >RIV/67985556:_____/23:00574036 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Tensor Chain Decomposition and Function Interpolation
Original language description
Tensor Chain (TC) decomposition represents a given tensor as a chain (circle) of order-3 tensors (wagons) connected through tensor contractions. In this paper, we show the link between the TC decomposition and a structured Tucker decompositions, and propose a variant of the Krylov-Levenberg-Marquardt optimization, tailored for this problem. Many extensions can be considered, here we only mention decomposition of tensor with missing entries, which enables the tensor completion. Performance of the proposed algorithm is demonstrated on tensor decomposition of the sampled Rosenbrock function. It can be better modeled both as TC and canonical polyadic (CP) decomposition, but with TC, the reconstruction is possible with a lower number of function values.
Czech name
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Czech description
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Project
<a href="/en/project/GA22-11101S" target="_blank" >GA22-11101S: Tensor Decomposition in Active Fault Diagnosis for Stochastic Large Scale Systems</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 22nd IEEE Statistical Signal Processing Workshop
ISBN
978-1-6654-5244-1
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
557-561
Publisher name
IEEE
Place of publication
Piscataway
Event location
Hanoi
Event date
Jul 2, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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