| bibtype |
J -
Journal Article
|
| ARLID |
0509948 |
| utime |
20240111141025.5 |
| mtime |
20191024235959.9 |
| WOS |
000492301000002 |
| SCOPUS |
85077750421 |
| DOI |
10.1109/LSP.2019.2943060 |
| title
(primary) (eng) |
Sensitivity in tensor decomposition |
| specification |
| page_count |
5 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0253212 |
| ISSN |
1070-9908 |
| title
|
IEEE Signal Processing Letters |
| volume_id |
26 |
| volume |
11 (2019) |
| page_num |
1653-1657 |
| publisher |
| name |
Institute of Electrical and Electronics Engineers |
|
|
| keyword |
PARAFAC |
| keyword |
convolutive neural networks |
| keyword |
tensor |
| 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 |
| garant |
K |
| 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 |
| ARLID |
cav_un_auth*0345929 |
| project_id |
GA17-00902S |
| agency |
GA ČR |
|
| abstract
(eng) |
Canonical polyadic (CP) tensor decomposition is an important task in many applications. Many times, the true tensor rank is not known, or noise is present, and in such situations, different existing CP decomposition algorithms provide very different results. In this paper, we introduce a notion of sensitivity of CP decomposition and suggest to use it as a side criterion (besides the fitting error)\nto evaluate different CP decomposition results. Next, we propose a novel variant of a Krylov-Levenberg-Marquardt CP decomposition algorithm which may serve for CP decomposition with a constraint on the sensitivity. In simulations, we decompose order-4 tensors that come from convolutional neural networks. We show that it is useful to combine the CP decomposition algorithms with an error-preserving correction. |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20201 |
| reportyear |
2020 |
| num_of_auth |
3 |
| mrcbC52 |
4 A hod sml 4ah 4as 20231122144345.3 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0301141 |
| mrcbC64 |
1 Department of Stochastic Informatics UTIA-B 20201 ENGINEERING, ELECTRICAL & ELECTRONIC |
| confidential |
S |
| contract |
| name |
Copyright receipt |
| date |
20190919 |
|
| mrcbC86 |
2 Article Engineering Electrical Electronic |
| mrcbC91 |
C |
| mrcbT16-e |
ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-j |
1.106 |
| mrcbT16-s |
1.145 |
| mrcbT16-B |
81.438 |
| mrcbT16-D |
Q1 |
| mrcbT16-E |
Q4 |
| arlyear |
2019 |
| mrcbTft |
\nSoubory v repozitáři: tichavsky-0509948.pdf, tichavsky-0509948-CopyrightReceipt.pdf |
| mrcbU14 |
85077750421 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000492301000002 WOS |
| mrcbU56 |
732 kB |
| mrcbU63 |
cav_un_epca*0253212 IEEE Signal Processing Letters 1070-9908 1558-2361 Roč. 26 č. 11 2019 1653 1657 Institute of Electrical and Electronics Engineers |
|