Blind source extraction in various ill-conditioned cases

Yuanqing Li; Jun Wang; Andrzej Cichocki

Blind source extraction in various ill-conditioned cases

Yuanqing Li, Jun Wang, Andrzej Cichocki

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

This paper discusses blind source extraction in various ill-conditioned cases based on a simple extraction network model. Extractability is first analyzed for the following ill-conditioned cases: the mixing matrix is square but singular, the number of sensors is smaller than that of sources, the number of sensors is larger than that of sources but the column rank of mixing matrix is deficient, and the number of sources is unknown and the column rank of mixing matrix is deficient. A necessary and sufficient condition for extractability is obtained. A cost function and an unsupervised learning algorithm for the extraction network model are developed. Simulation results are also presented to show the validity of the theoretical results and the performance and characteristics of the learning algorithm.

Original language	English
Title of host publication	2004 International Conference on Communications, Circuits and Systems
Pages	1004-1007
Volume	2
Publication status	Published - 2004
Externally published	Yes
Event	2004 International Conference on Communications, Circuits and Systems - Chengdu, China Duration: 27 Jun 2004 → 29 Jun 2004

Publication series

Name
Volume	2

Conference

Conference	2004 International Conference on Communications, Circuits and Systems
Place	China
City	Chengdu
Period	27/06/04 → 29/06/04

Research Keywords

Adaptive algorithm
Blind extraction
Cost function
Extractability
Ill-conditioned cases

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@inproceedings{63c570d7329746be98389fb7a215dfa9,

title = "Blind source extraction in various ill-conditioned cases",

abstract = "This paper discusses blind source extraction in various ill-conditioned cases based on a simple extraction network model. Extractability is first analyzed for the following ill-conditioned cases: the mixing matrix is square but singular, the number of sensors is smaller than that of sources, the number of sensors is larger than that of sources but the column rank of mixing matrix is deficient, and the number of sources is unknown and the column rank of mixing matrix is deficient. A necessary and sufficient condition for extractability is obtained. A cost function and an unsupervised learning algorithm for the extraction network model are developed. Simulation results are also presented to show the validity of the theoretical results and the performance and characteristics of the learning algorithm.",

keywords = "Adaptive algorithm, Blind extraction, Cost function, Extractability, Ill-conditioned cases",

author = "Yuanqing Li and Jun Wang and Andrzej Cichocki",

year = "2004",

language = "English",

isbn = "0780386477",

volume = "2",

pages = "1004--1007",

booktitle = "2004 International Conference on Communications, Circuits and Systems",

note = "2004 International Conference on Communications, Circuits and Systems ; Conference date: 27-06-2004 Through 29-06-2004",

}

TY - GEN

T1 - Blind source extraction in various ill-conditioned cases

AU - Li, Yuanqing

AU - Wang, Jun

AU - Cichocki, Andrzej

PY - 2004

Y1 - 2004

N2 - This paper discusses blind source extraction in various ill-conditioned cases based on a simple extraction network model. Extractability is first analyzed for the following ill-conditioned cases: the mixing matrix is square but singular, the number of sensors is smaller than that of sources, the number of sensors is larger than that of sources but the column rank of mixing matrix is deficient, and the number of sources is unknown and the column rank of mixing matrix is deficient. A necessary and sufficient condition for extractability is obtained. A cost function and an unsupervised learning algorithm for the extraction network model are developed. Simulation results are also presented to show the validity of the theoretical results and the performance and characteristics of the learning algorithm.

AB - This paper discusses blind source extraction in various ill-conditioned cases based on a simple extraction network model. Extractability is first analyzed for the following ill-conditioned cases: the mixing matrix is square but singular, the number of sensors is smaller than that of sources, the number of sensors is larger than that of sources but the column rank of mixing matrix is deficient, and the number of sources is unknown and the column rank of mixing matrix is deficient. A necessary and sufficient condition for extractability is obtained. A cost function and an unsupervised learning algorithm for the extraction network model are developed. Simulation results are also presented to show the validity of the theoretical results and the performance and characteristics of the learning algorithm.

KW - Adaptive algorithm

KW - Blind extraction

KW - Cost function

KW - Extractability

KW - Ill-conditioned cases

UR - http://www.scopus.com/inward/record.url?scp=11244296149&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-11244296149&origin=recordpage

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 0780386477

SN - 9780780386471

VL - 2

SP - 1004

EP - 1007

BT - 2004 International Conference on Communications, Circuits and Systems

T2 - 2004 International Conference on Communications, Circuits and Systems

Y2 - 27 June 2004 through 29 June 2004

ER -

Blind source extraction in various ill-conditioned cases

Abstract

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Research Keywords

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