DocumentCode
1572132
Title
Notice of Violation of IEEE Publication Principles
Observations on Liner Prediction Based Blind Source Extraction Algorithms
Author
Gan, Min ; Wang, Hongfa ; Yue, Guoying ; Xie, Nan ; Zhang, Haibo
Author_Institution
Dept. of Comput. & Inf. Eng., Zhejiang Water Conservancy & Hydropower Coll., Hangzhou
fYear
2008
Firstpage
373
Lastpage
376
Abstract
Notice of Violation of IEEE Publication Principles
"Observations on Liner Prediction Based Blind Source Extraction Algorithms"
by Min Gan, Hongfa Wang, Guoying Yue, Nan Xie, Haibo Zhang
in the Proceedings of the Seventh IEEE/ACIS International Conference on Computer and Information Science (ICIS 2008), May 2008, pp. 373-376
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"Linear Prediction Based Blind Source Extraction Algorithms in Practical Applications"
by Zhi-Lin Zhang, Liqing Zhang
in the Proceedings of the 7th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2007), 2007, pp. 309-316
As we see, blind source extraction (BSE) is advantageous over blind source separation (BSS) when obtaining some underlying source signals from high dimensional signals. Among a variety of BSE algorithms, a large number of algorithms are based on linear prediction (LP-BSE). In this paper we reveal that they are, in nature, minor component analysis (MCA) algorithms, and thus they have some problems that are inherent in MCA algorithms. We also find a switch phenomenon of online LP-BSE algorithms, showing that different parts of a singly extracted signal are the counterparts of different source signals. The two issues should be noticed when one applies these algorithms to practical applications. Computer simulations are given to co- nfirm these observations.
"Observations on Liner Prediction Based Blind Source Extraction Algorithms"
by Min Gan, Hongfa Wang, Guoying Yue, Nan Xie, Haibo Zhang
in the Proceedings of the Seventh IEEE/ACIS International Conference on Computer and Information Science (ICIS 2008), May 2008, pp. 373-376
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"Linear Prediction Based Blind Source Extraction Algorithms in Practical Applications"
by Zhi-Lin Zhang, Liqing Zhang
in the Proceedings of the 7th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2007), 2007, pp. 309-316
As we see, blind source extraction (BSE) is advantageous over blind source separation (BSS) when obtaining some underlying source signals from high dimensional signals. Among a variety of BSE algorithms, a large number of algorithms are based on linear prediction (LP-BSE). In this paper we reveal that they are, in nature, minor component analysis (MCA) algorithms, and thus they have some problems that are inherent in MCA algorithms. We also find a switch phenomenon of online LP-BSE algorithms, showing that different parts of a singly extracted signal are the counterparts of different source signals. The two issues should be noticed when one applies these algorithms to practical applications. Computer simulations are given to co- nfirm these observations.
Keywords
blind source separation; blind source extraction algorithm; blind source separation; computer simulation; linear prediction; minor component analysis algorithm; signal extraction; Blind source separation; Data mining; Electroencephalography; Gallium nitride; Information science; Prediction algorithms; Source separation; Switches; Vectors; Water conservation;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Information Science, 2008. ICIS 08. Seventh IEEE/ACIS International Conference on
Conference_Location
Portland, OR
Print_ISBN
978-0-7695-3131-1
Type
conf
DOI
10.1109/ICIS.2008.102
Filename
4529848
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