| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0492879 |
| utime |
20240111141005.5 |
| mtime |
20180905235959.9 |
| SCOPUS |
85048543885 |
| DOI |
10.1007/978-3-319-93764-9_16 |
| title
(primary) (eng) |
Orthogonally-Constrained Extraction of Independent Non-Gaussian Component from Non-Gaussian Background Without ICA |
| specification |
| page_count |
10 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0492878 |
| ISBN |
978-3-319-93763-2 |
| ISSN |
0302-9743 |
| title
|
Latent Variable Analysis and Signal Separation |
| page_num |
161-170 |
| publisher |
| place |
Cham |
| name |
Springer |
| year |
2018 |
|
| editor |
| name1 |
Deville |
| name2 |
Yannick |
|
| editor |
| name1 |
Gannot |
| name2 |
Sharon |
|
| editor |
| name1 |
Mason |
| name2 |
Russell |
|
| editor |
| name1 |
Plumbley |
| name2 |
Mark D. |
|
| editor |
|
|
| keyword |
Independent Component Analysis |
| keyword |
Blind source separation |
| keyword |
blind source extraction |
| author
(primary) |
| ARLID |
cav_un_auth*0230113 |
| name1 |
Koldovský |
| name2 |
Z. |
| country |
CZ |
|
| 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*0363725 |
| name1 |
Ono |
| name2 |
N. |
| country |
JP |
|
| source |
|
| cas_special |
| project |
| project_id |
GA17-00902S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0345929 |
|
| abstract
(eng) |
We propose a new algorithm for Independent Component Extraction that extracts one non-Gaussian component and is capable to exploit the non-Gaussianity of background signals without decomposing them into independent components. The algorithm is suitable for situations when the signal to be extracted is determined through initialization, it shows an extra stable convergence when the target component is dominant. In simulations, the proposed method is compared with Natural Gradient and One-unit FastICA, and it yields improved results in terms of the Signal-to-Interference ratio and the number of successful extractions. |
| action |
| ARLID |
cav_un_auth*0363587 |
| name |
Latent Variable Analysis and Signal Separation |
| dates |
20180702 |
| place |
Guilford |
| country |
GB |
| mrcbC20-s |
20180705 |
|
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2019 |
| num_of_auth |
3 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0286552 |
| confidential |
S |
| mrcbT16-s |
0.339 |
| mrcbT16-4 |
Q2 |
| mrcbT16-E |
Q2 |
| arlyear |
2018 |
| mrcbU14 |
85048543885 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU56 |
282 kB |
| mrcbU63 |
cav_un_epca*0492878 Latent Variable Analysis and Signal Separation 978-3-319-93763-2 0302-9743 1611-3349 161 170 Cham Springer 2018 Lecture Notes in Computer Science 10891 |
| mrcbU67 |
340 Deville Yannick |
| mrcbU67 |
340 Gannot Sharon |
| mrcbU67 |
340 Mason Russell |
| mrcbU67 |
340 Plumbley Mark D. |
| mrcbU67 |
340 Ward Dominic |
|