• Je něco špatně v tomto záznamu ?

A hybrid technique for blind separation of non-gaussian and time-correlated sources using a multicomponent approach

P Tichavsky, Z Koldovsky, A Yeredor, G Gomez-Herrero, E Doron

. 2008 ; 19 (3) : 421-430.

Jazyk angličtina Země Spojené státy americké

Perzistentní odkaz   https://www.medvik.cz/link/bmc11003033

Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient fastICA (EFICA) offers an asymptotically optimal solution to this problem when all of the sources obey a generalized Gaussian distribution, at most one of them is Gaussian, and each is independent and identically distributed (i.i.d.) in time. Likewise, weights-adjusted second-order blind identification (WASOBI) is asymptotically optimal when all the sources are Gaussian and can be modeled as autoregressive (AR) processes with distinct spectra. Nevertheless, real-life mixtures are likely to contain both Gaussian AR and non-Gaussian i.i.d. sources, rendering WASOBI and EFICA severely suboptimal. In this paper, we propose a novel scheme for combining the strengths of EFICA and WASOBI in order to deal with such hybrid mixtures. Simulations show that our approach outperforms competing algorithms designed for separating similar mixtures.

Citace poskytuje Crossref.org

000      
02322naa 2200325 a 4500
001      
bmc11003033
003      
CZ-PrNML
005      
20121105124422.0
008      
110225s2008 xxu e eng||
009      
AR
024    __
$a 10.1109/tnn.2007.908648 $2 doi
035    __
$a (PubMed)18334362
040    __
$a ABA008 $b cze $c ABA008 $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a xxu
100    1_
$a Tichavský, Petr. $7 _AN059680
245    12
$a A hybrid technique for blind separation of non-gaussian and time-correlated sources using a multicomponent approach / $c P Tichavsky, Z Koldovsky, A Yeredor, G Gomez-Herrero, E Doron
314    __
$a Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, 182 08 Prague8, Czech Republic. tichavsk@utia.cas.cz
520    9_
$a Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient fastICA (EFICA) offers an asymptotically optimal solution to this problem when all of the sources obey a generalized Gaussian distribution, at most one of them is Gaussian, and each is independent and identically distributed (i.i.d.) in time. Likewise, weights-adjusted second-order blind identification (WASOBI) is asymptotically optimal when all the sources are Gaussian and can be modeled as autoregressive (AR) processes with distinct spectra. Nevertheless, real-life mixtures are likely to contain both Gaussian AR and non-Gaussian i.i.d. sources, rendering WASOBI and EFICA severely suboptimal. In this paper, we propose a novel scheme for combining the strengths of EFICA and WASOBI in order to deal with such hybrid mixtures. Simulations show that our approach outperforms competing algorithms designed for separating similar mixtures.
650    _2
$a algoritmy $7 D000465
650    _2
$a lidé $7 D006801
650    _2
$a statistické modely $7 D015233
650    _2
$a neuronové sítě $7 D016571
650    _2
$a počítačové zpracování signálu $7 D012815
650    _2
$a čas $7 D013995
650    _2
$a financování organizované $7 D005381
700    1_
$a Koldovský, Zbyněk. $7 _AN059681
700    1_
$a Yeredor, Arie
700    1_
$a Gomez-Herrero, German
700    1_
$a Doron, Eran
773    0_
$t IEEE Transactions on Neural Networks $w MED00002175 $g Roč. 19, č. 3 (2008), s. 421-430 $x 1045-9227
910    __
$a ABA008 $b x $y 1
990    __
$a 20110413114032 $b ABA008
991    __
$a 20121105124429 $b ABA008
999    __
$a ok $b bmc $g 830433 $s 695025
BAS    __
$a 3
BMC    __
$a 2008 $b 19 $c 3 $d 421-430 $i 1045-9227 $m IEEE transactions on neural networks $n IEEE Trans Neural Netw $x MED00002175
LZP    __
$a 2011-2B/dkme
Zpět

Najít záznam

Citační ukazatele

Možnosti archivace