| bibtype |
M -
Monography Chapter
|
| ARLID |
0444036 |
| utime |
20240103210055.0 |
| mtime |
20150526235959.9 |
| DOI |
10.1016/B978-0-12-802806-3.00002-6 |
| title
(primary) (eng) |
Improved variants of the FastICA algorithm |
| specification |
| page_count |
22 s. |
| media_type |
P |
| book_pages |
296 |
|
| serial |
| ARLID |
cav_un_epca*0444035 |
| ISBN |
978-0-12-802806-3 |
| title
|
Advances in Independent Component Analysis and Learning Machines |
| page_num |
53-74 |
| publisher |
| place |
Londýn |
| name |
Elsevier |
| year |
2015 |
|
| editor |
|
| editor |
|
| editor |
| name1 |
Laaksonen |
| name2 |
Jorma |
|
| editor |
| name1 |
Lampinen |
| name2 |
Jouko |
|
|
| keyword |
independent component analysis |
| keyword |
blind source separation |
| keyword |
FastICA |
| keyword |
efica |
| keyword |
Cramer-Rao lower bound |
| author
(primary) |
| ARLID |
cav_un_auth*0108100 |
| name1 |
Koldovský |
| name2 |
Zbyněk |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
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*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. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA14-13713S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0303443 |
|
| abstract
(eng) |
The article presents a survey of improved variants of the famous FastICA algorithm for Independent Component Analysis. Variants of the algorithm tailored to separate mixtures of stationary non-Gaussian signals and mixtures of nonstationary (block-wise stationary) non-Gaussian signals are described. Performance analyses of the algorithms are given and compared to the respective Cramer-Rao lower bounds. The behavior of FastICA variants when additive noise is present in the signal mixture is studied through a bias analysis. |
| reportyear |
2016 |
| RIV |
BB |
| num_of_auth |
2 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0246783 |
| confidential |
S |
| arlyear |
2015 |
| mrcbU63 |
cav_un_epca*0444035 Advances in Independent Component Analysis and Learning Machines 978-0-12-802806-3 53 74 Londýn Elsevier 2015 |
| mrcbU67 |
Bingham Ella 340 |
| mrcbU67 |
Kaski Samuel 340 |
| mrcbU67 |
Laaksonen Jorma 340 |
| mrcbU67 |
Lampinen Jouko 340 |
|