Tensor Train Approximation of Multivariate Functions
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43977847" target="_blank" >RIV/49777513:23520/24:43977847 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.23919/EUSIPCO63174.2024.10715191" target="_blank" >https://doi.org/10.23919/EUSIPCO63174.2024.10715191</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/EUSIPCO63174.2024.10715191" target="_blank" >10.23919/EUSIPCO63174.2024.10715191</a>
Alternative languages
Result language
angličtina
Original language name
Tensor Train Approximation of Multivariate Functions
Original language description
The tensor train is a popular model for approximating high-dimensional rectangular data structures that cannot fit in any computer memory due to their size. The tensor train can approximate complex functions with many variables in the continuous domain. The traditional method for obtaining the tensor train model is based on a skeleton decomposition, which is better known for matrices. The skeleton (cross) decomposition has the property that the tensor approximation is accurate on certain tensor fibers but may be poor on other fibers. In this paper, we propose a technique for fitting a tensor train to an arbitrary number of tensor fibers, allowing flexible modeling of multivariate functions that contain noise. Two examples are studied: a noisy Rosenbrock function and a noisy quadratic function, both of order 20.n
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20205 - Automation and control systems
Result continuities
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
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 32nd European Signal Processing Conference (EUSIPCO)
ISBN
979-8-3315-1977-3
ISSN
2219-5491
e-ISSN
2076-1465
Number of pages
5
Pages from-to
2262-2266
Publisher name
IEEE
Place of publication
Lyon, France
Event location
Lyon, France
Event date
Aug 26, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001349787000452