| bibtype |
J -
Journal Article
|
| ARLID |
0500107 |
| utime |
20240111141013.8 |
| mtime |
20190118235959.9 |
| SCOPUS |
85058883993 |
| WOS |
000455721400005 |
| DOI |
10.1109/TSP.2018.2887192 |
| title
(primary) (eng) |
Error Preserving Correction: A Method for CP Decomposition at a Target Error Bound |
| specification |
| page_count |
16 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256727 |
| ISSN |
1053-587X |
| title
|
IEEE Transactions on Signal Processing |
| volume_id |
67 |
| volume |
5 (2019) |
| page_num |
1175-1190 |
|
| keyword |
Canonical polyadic decomposition |
| keyword |
parallel factor analysis |
| keyword |
tensor decomposition |
| author
(primary) |
| ARLID |
cav_un_auth*0274170 |
| name1 |
Phan |
| name2 |
A. H. |
| country |
JP |
|
| author
|
| ARLID |
cav_un_auth*0101212 |
| name1 |
Tichavský |
| name2 |
Petr |
| full_dept (cz) |
Stochastická informatika |
| full_dept |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department |
SI |
| institution |
UTIA-B |
| full_dept |
Department of Stochastic Informatics |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0274171 |
| name1 |
Cichocki |
| name2 |
A. |
| country |
JP |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA17-00902S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0345929 |
|
| abstract
(eng) |
In CANDECOMP/PARAFAC tensor decomposition, degeneracy often occurs in some difficult scenarios, especially, when the rank exceeds the tensor dimension, or when the loading components are highly collinear in several or all modes, or when CPD does not have an optimal solution. In such cases, norms of some rank-1 tensors become significantly large and cancel each other. This makes algorithms getting stuck in local minima while running a huge number of iterations does not improve the decomposition. In this paper, we propose an error preservation correction method to deal with such problem. Our aim is to seek an alternative tensor, which preserves the approximation error, but norms of rank-1 tensor components of the new tensor are minimized. Alternating and all-at-once correction algorithms have been developed for the problem. In addition, we propose a novel CPD with a bound constraint on the norm of the rank-one tensors. The method can be useful for decomposing tensors that cannot be performed by traditional algorithms. Finally, we demonstrate an application of the proposed method in image denoising and decomposition of the weight tensors in convolutional neural networks. |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2020 |
| num_of_auth |
3 |
| mrcbC52 |
4 A hod 4ah 20231122143746.2 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0293323 |
| mrcbC64 |
1 Department of Stochastic Informatics UTIA-B 20201 ENGINEERING, ELECTRICAL & ELECTRONIC |
| confidential |
S |
| mrcbC86 |
2 Article Engineering Electrical Electronic |
| mrcbC91 |
C |
| mrcbT16-e |
ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-j |
1.718 |
| mrcbT16-s |
2.098 |
| mrcbT16-B |
91.914 |
| mrcbT16-D |
Q1* |
| mrcbT16-E |
Q1* |
| arlyear |
2019 |
| mrcbTft |
\nSoubory v repozitáři: tichavsky-0500107.pdf |
| mrcbU14 |
85058883993 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000455721400005 WOS |
| mrcbU56 |
3.1 MB |
| mrcbU63 |
cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 67 č. 5 2019 1175 1190 |
|