| bibtype |
J -
Journal Article
|
| ARLID |
0448255 |
| utime |
20240103210755.4 |
| mtime |
20151022235959.9 |
| SCOPUS |
84944696619 |
| WOS |
000362746500004 |
| DOI |
10.1109/TSP.2015.2458785 |
| title
(primary) (eng) |
Tensor Deflation for CANDECOMP/PARAFAC - Part I: Alternating Subspace Update Algorithm |
| specification |
| page_count |
15 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256727 |
| ISSN |
1053-587X |
| title
|
IEEE Transactions on Signal Processing |
| volume_id |
63 |
| volume |
22 (2015) |
| page_num |
5924-5938 |
|
| keyword |
Canonical polyadic decomposition |
| keyword |
tensor deflation |
| keyword |
tensor tracking |
| author
(primary) |
| ARLID |
cav_un_auth*0274170 |
| name1 |
Phan |
| name2 |
A. H. |
| country |
JP |
|
| author
|
| ARLID |
cav_un_auth*0101212 |
| full_dept (cz) |
Stochastická informatika |
| full_dept |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department |
SI |
| full_dept |
Department of Stochastic Informatics |
| name1 |
Tichavský |
| name2 |
Petr |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0274171 |
| name1 |
Cichocki |
| name2 |
A. |
| country |
JP |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0303443 |
| project_id |
GA14-13713S |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
CANDECOMP/PARAFAC (CP) approximates multiway data by sum of rank-1 tensors. Unlike matrix decomposition, the procedure which estimates the best rank-tensor approximation through R sequential best rank-1 approximations does not work for tensors, because the deflation does not always reduce the tensor rank. In this paper, we propose a novel deflation method for the problem. When one factor matrix of a rank-CP decomposition is of full column rank, the decomposition can be performed through (R-1) rank-1 reductions. At each deflation stage, the residue tensor is constrained to have a reduced multilinear rank. For decomposition of order-3 tensors of size RxRxR and rank-R estimation of one rank-1 tensor has a computational cost of O(R^3) per iteration which is lower than the cost O(R^4) of the ALS algorithm for the overall CP decomposition. The method can be extended to tracking one or a few rank-one tensors of slow changes, or inspect variations of common patterns in individual datasets. |
| RIV |
BB |
| reportyear |
2016 |
| num_of_auth |
3 |
| mrcbC52 |
4 A hod 4ah 20231122141206.8 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0250564 |
| mrcbC64 |
1 Department of Stochastic Informatics UTIA-B 20201 ENGINEERING, ELECTRICAL & ELECTRONIC |
| confidential |
S |
| mrcbT16-e |
ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-j |
1.527 |
| mrcbT16-s |
1.581 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
89.565 |
| mrcbT16-C |
86.965 |
| mrcbT16-D |
Q1 |
| mrcbT16-E |
Q1* |
| arlyear |
2015 |
| mrcbTft |
\nSoubory v repozitáři: tichavsky-0448255.pdf |
| mrcbU14 |
84944696619 SCOPUS |
| mrcbU34 |
000362746500004 WOS |
| mrcbU63 |
cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 63 č. 22 2015 5924 5938 |
|