0598049 20250317085914.920240910235959.9 85208427467 001349787000452 10.23919/EUSIPCO63174.2024.10715191 5 s. E cav_un_epca*0598048978-9-4645-9361-72262-2266LyonEURASIP2024 tensor train multivariate function function interpolation cav_un_auth*0101212 Tichavský Petr UTIA-B Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics 80 Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0434606 Straka O. CZ 20 https://library.utia.cas.cz/separaty/2024/SI/tichavsky-0598049.pdf https://ieeexplore.ieee.org/document/10715191 GA22-11101S GA ČR CZ cav_un_auth*0435406 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. cav_un_auth*0472263 European Signal Processing Conference 2024 /32./ 20240826 20240830 Lyon FR BB 20000 20200 20201 2025 2 PO RVO:67985556 https://hdl.handle.net/11104/0356116 S 2024 85208427467 SCOPUS PUBMED 001349787000452 WOS cav_un_epca*0598048 EUSIPCO 2024 EURASIP 2024 Lyon 2262 2266 978-9-4645-9361-7