| bibtype |
J -
Journal Article
|
| ARLID |
0396775 |
| utime |
20240111140835.2 |
| mtime |
20131031235959.9 |
| WOS |
000324342900017 |
| DOI |
10.1109/TSP.2013.2269046 |
| title
(primary) (eng) |
CANDECOMP/PARAFAC Decomposition of High-Order Tensors Through Tensor Reshaping |
| specification |
| page_count |
14 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256727 |
| ISSN |
1053-587X |
| title
|
IEEE Transactions on Signal Processing |
| volume_id |
61 |
| volume |
19 (2013) |
| page_num |
4847-4860 |
|
| keyword |
tensor factorization |
| keyword |
canonical polyadic decomposition |
| keyword |
Cramer-Rao bound |
| 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 |
|
| cas_special |
| project |
| project_id |
GA102/09/1278 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0253174 |
|
| abstract
(eng) |
In general, algorithms for order-3 CANDECOMP/ PARAFAC (CP), also coined canonical polyadic decomposition (CPD), are easy to implement and can be extended to higher order CPD. Unfortunately, the algorithms become computationally demanding, and they are often not applicable to higher order and relatively large scale tensors. In this paper, by exploiting the uniqueness of CPD and the relation of a tensor in Kruskal form and its unfolded tensor, we propose a fast approach to deal with this problem. Instead of directly factorizing the high order data tensor, the method decomposes an unfolded tensor with lower order, e.g., order-3 tensor. On the basis of the order-3 estimated tensor, a structured Kruskal tensor, of the same dimension as the data tensor, is then generated, and decomposed to find the final solution using fast algorithms for the structured CPD. In addition, strategies to unfold tensors are suggested and practically verified in the paper. |
| reportyear |
2014 |
| RIV |
BB |
| num_of_auth |
3 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0225514 |
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ENGINEERINGELECTRICALELECTRONIC |
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3.592 |
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0.439 |
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7.II |
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0.07199 |
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1.62 |
| mrcbT16-k |
22913 |
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508 |
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2.074 |
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90.927 |
| mrcbT16-D |
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| mrcbT16-E |
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| arlyear |
2013 |
| mrcbU34 |
000324342900017 WOS |
| mrcbU56 |
3.98 MB |
| mrcbU63 |
cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 61 č. 19 2013 4847 4860 |
|