Tensor Train Approximation of Multivariate Functions

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Tensor Train Approximation of Multivariate Functions

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Tensor Train Approximation of Multivariate Functions

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GA22-11101S" target="_blank" >GA22-11101S: Tenzorový rozklad v aktivní diagnostice poruch pro stochastické rozlehlé systémy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 32nd European Signal Processing Conference (EUSIPCO)

ISBN

979-8-3315-1977-3

ISSN

2219-5491

e-ISSN

2076-1465

Počet stran výsledku

5

Strana od-do

2262-2266

Název nakladatele

IEEE

Místo vydání

Lyon, France

Místo konání akce

Lyon, France

Datum konání akce

26. 8. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001349787000452