| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0598049 |
| utime |
20250317085914.9 |
| mtime |
20240910235959.9 |
| SCOPUS |
85208427467 |
| WOS |
001349787000452 |
| DOI |
10.23919/EUSIPCO63174.2024.10715191 |
| title
(primary) (eng) |
Tensor Train Approximation of Multivariate Functions |
| specification |
| page_count |
5 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0598048 |
| ISBN |
978-9-4645-9361-7 |
| title
|
EUSIPCO 2024 |
| page_num |
2262-2266 |
| publisher |
| place |
Lyon |
| name |
EURASIP |
| year |
2024 |
|
|
| keyword |
tensor train |
| keyword |
multivariate function |
| keyword |
function interpolation |
| author
(primary) |
| ARLID |
cav_un_auth*0101212 |
| name1 |
Tichavský |
| name2 |
Petr |
| institution |
UTIA-B |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
SI |
| full_dept |
Department of Stochastic Informatics |
| share |
80 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0434606 |
| name1 |
Straka |
| name2 |
O. |
| country |
CZ |
| share |
20 |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA22-11101S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0435406 |
|
| abstract
(eng) |
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 |
| action |
| ARLID |
cav_un_auth*0472263 |
| name |
European Signal Processing Conference 2024 /32./ |
| dates |
20240826 |
| mrcbC20-s |
20240830 |
| place |
Lyon |
| country |
FR |
|
| RIV |
BB |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20201 |
| reportyear |
2025 |
| num_of_auth |
2 |
| presentation_type |
PO |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0356116 |
| confidential |
S |
| arlyear |
2024 |
| mrcbU14 |
85208427467 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001349787000452 WOS |
| mrcbU63 |
cav_un_epca*0598048 EUSIPCO 2024 EURASIP 2024 Lyon 2262 2266 978-9-4645-9361-7 |
|