| bibtype |
J -
Journal Article
|
| ARLID |
0460710 |
| utime |
20240103212406.8 |
| mtime |
20160714235959.9 |
| SCOPUS |
84978100769 |
| WOS |
000379694800005 |
| DOI |
10.1109/LSP.2016.2577383 |
| title
(primary) (eng) |
Partitioned Alternating Least Squares Technique for Canonical Polyadic 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 |
23 |
| volume |
7 (2016) |
| page_num |
993-997 |
| publisher |
| name |
Institute of Electrical and Electronics Engineers |
|
|
| keyword |
canonical polyadic decomposition |
| keyword |
PARAFAC |
| keyword |
tensor decomposition |
| author
(primary) |
| ARLID |
cav_un_auth*0101212 |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
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*0274170 |
| name1 |
Phan |
| name2 |
A. H. |
| country |
JP |
|
| 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) |
Canonical polyadic decomposition (CPD), also known as parallel factor analysis, is a representation of a given tensor as a sum of rank-one components. Traditional method for accomplishing CPD is the alternating least squares (ALS) algorithm. Convergence of ALS is known to be slow, especially when some factor matrices of the tensor contain nearly collinear columns. We propose a novel variant of this technique, in which the factor matrices are partitioned into blocks, and each iteration jointly updates blocks of different factor matrices. Each partial optimization is quadratic and can be done in closed form. The algorithm alternates between different random partitionings of the matrices. As a result, a faster convergence is achieved. Another improvement can be obtained when the method is combined with the enhanced line search of Rajih et al. Complexity per iteration is between those of the ALS and the Levenberg–Marquardt (damped Gauss–Newton) method. |
| RIV |
BB |
| reportyear |
2017 |
| num_of_auth |
3 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0261531 |
| confidential |
S |
| mrcbC86 |
2 Article Engineering Electrical Electronic |
| mrcbT16-e |
ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-j |
0.964 |
| mrcbT16-s |
0.798 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
73.144 |
| mrcbT16-D |
Q2 |
| mrcbT16-E |
Q2 |
| arlyear |
2016 |
| mrcbU14 |
84978100769 SCOPUS |
| mrcbU34 |
000379694800005 WOS |
| mrcbU63 |
cav_un_epca*0253212 IEEE Signal Processing Letters 1070-9908 1558-2361 Roč. 23 č. 7 2016 993 997 Institute of Electrical and Electronics Engineers |
|