| bibtype |
J -
Journal Article
|
| ARLID |
0541614 |
| utime |
20250310155920.6 |
| mtime |
20210409235959.9 |
| WOS |
000637533400010 |
| SCOPUS |
85100948273 |
| DOI |
10.1109/JSTSP.2021.3059521 |
| title
(primary) (eng) |
Krylov-Levenberg-Marquardt Algorithm for Structured Tucker Tensor Decompositions |
| specification |
| page_count |
10 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0344461 |
| ISSN |
1932-4553 |
| title
|
IEEE Journal on Selected Topics in Signal Processing |
| volume_id |
15 |
| volume |
3 (2021) |
| page_num |
550-559 |
| publisher |
| name |
Institute of Electrical and Electronics Engineers |
|
|
| keyword |
canonical polyadic tensor decomposition |
| keyword |
parallel factor analysis |
| keyword |
tensor chain |
| keyword |
sensitivity |
| 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 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0382249 |
| name1 |
Phan |
| name2 |
A. H. |
| country |
RU |
|
| author
|
| ARLID |
cav_un_auth*0382250 |
| name1 |
Cichocki |
| name2 |
A. |
| country |
RU |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA20-17720S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0396617 |
|
| abstract
(eng) |
Structured Tucker tensor decomposition models complete or incomplete multiway data sets (tensors), where the core tensor and the factor matrices can obey different constraints. The model includes block-term decomposition or canonical polyadic decomposition as special cases. We propose a very flexible optimization method for the structured Tucker decomposition problem, based on the second-order Levenberg-Marquardt optimization, using an approximation of the Hessian matrix by the Krylov subspace method. An algorithm with limited sensitivity of the decomposition is included. The proposed algorithm is shown to perform well in comparison to existing tensor decomposition methods.\n |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20201 |
| reportyear |
2022 |
| num_of_auth |
3 |
| mrcbC52 |
2 R hod 4 4rh 4 20250310155350.5 4 20250310155920.6 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0319267 |
| confidential |
S |
| mrcbC86 |
2 Article Engineering Electrical Electronic |
| mrcbC91 |
C |
| mrcbT16-e |
ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-j |
2.578 |
| mrcbT16-s |
3.227 |
| mrcbT16-D |
Q1* |
| mrcbT16-E |
Q1* |
| arlyear |
2021 |
| mrcbTft |
\nSoubory v repozitáři: tichavsky-0541614.pdf |
| mrcbU14 |
85100948273 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000637533400010 WOS |
| mrcbU63 |
cav_un_epca*0344461 IEEE Journal on Selected Topics in Signal Processing 1932-4553 1941-0484 Roč. 15 č. 3 2021 550 559 Institute of Electrical and Electronics Engineers |
|