| bibtype |
J -
Journal Article
|
| ARLID |
0396774 |
| utime |
20240111140835.2 |
| mtime |
20131031235959.9 |
| WOS |
000324342900016 |
| DOI |
10.1109/TSP.2013.2269903 |
| title
(primary) (eng) |
Fast Alternating LS Algorithms for High Order CANDECOMP/PARAFAC Tensor Factorizations |
| specification |
| page_count |
13 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 |
4834-4846 |
|
| keyword |
Canonical polyadic decomposition |
| 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 |
|
| cas_special |
| project |
| project_id |
GA102/09/1278 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0253174 |
|
| abstract
(eng) |
CANDECOMP/PARAFAC (CP) has found numerous applications in wide variety of areas such as in chemometrics, telecommunication, data mining, neuroscience, separated representations. For an order- tensor, most CP algorithms can be computationally demanding due to computation of gradients which are related to products between tensor unfoldings and Khatri-Rao products of all factor matrices except one. These products have the largest workload in most CP algorithms. In this paper, we propose a fast method to deal with this issue. Themethod also reduces the extra memory requirements of CP algorithms. As a result, we can accelerate the standard alternating CP algorithms 20–30 times for order-5 and order-6 tensors, and even higher ratios can be obtained for higher order tensors (e.g., N>=10). The proposed method is more efficient than the state-of-the-art ALS algorithm which operates two modes at a time (ALSo2) in the Eigenvector PLS toolbox, especially for tensors with order N>=5 and high rank. |
| reportyear |
2014 |
| RIV |
BB |
| num_of_auth |
3 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0225512 |
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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 |
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22913 |
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| mrcbT16-D |
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| mrcbT16-E |
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| arlyear |
2013 |
| mrcbU34 |
000324342900016 WOS |
| mrcbU56 |
4.2MB |
| mrcbU63 |
cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 61 č. 19 2013 4834 4846 |
|