Comparative Study of Two Independent Component Analysis Using Reference Signal Methods

被引：0
|
作者：
Mi, Jian-Xun [1 ,2 ]
Yang, Yanxin [3 ]
机构：
[1] Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Shenzhen, Guangdong, Peoples R China
[2] Key Lab Network Oriented Intelligent Comp, Shenzhen, Peoples R China
[3] Yunnan Agr Univ, Fac Engn & Technol, Kunming, Yunnan, Peoples R China
来源：
EMERGING INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS | 2012年 / 304卷
关键词：
Independent component analysis; ICA-R; FICAR; EXTRACTION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Independent Component Analysis (ICA) using reference signal is a useful tool for extracting a desired independent component (IC). Reference signal is served as a priori information to conduct ICA to converge to the local extreme point related to a desired IC. There are two methods can perform ICA using reference signal, namely ICA with reference (ICA-R) and fast ICA with reference signal (FICAR). In this paper, we present a comparative assessment of the two methods to highlight their respective characteristics.
引用
收藏
页码：93 / +
页数：3
相关论文
共 50 条
  • [1] A comparative study and improvement of two ICA using reference signal methods
    Mi, Jian-Xun
    Xu, Yong
    [J]. NEUROCOMPUTING, 2014, 137 : 157 - 164
  • [2] Spatial density reduction in the study of the ECG signal using independent component analysis
    Arani, Amirali Shayan
    Zhu, Yi
    Zhang, Wanping
    Jung, Tzyy-Ping
    Duann, Jeng-Ren
    Makeig, Scott
    Cheng, Chung-Kuan
    [J]. 2007 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-16, 2007, : 5497 - +
  • [3] Signal separation method using independent component analysis
    Yoshioka, M
    Omatu, S
    [J]. ICONIP'98: THE FIFTH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING JOINTLY WITH JNNS'98: THE 1998 ANNUAL CONFERENCE OF THE JAPANESE NEURAL NETWORK SOCIETY - PROCEEDINGS, VOLS 1-3, 1998, : 753 - 756
  • [4] Signal separation method using independent component analysis
    Yoshioka, M
    Omatu, S
    [J]. ICONIP'98: THE FIFTH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING JOINTLY WITH JNNS'98: THE 1998 ANNUAL CONFERENCE OF THE JAPANESE NEURAL NETWORK SOCIETY - PROCEEDINGS, VOLS 1-3, 1998, : 891 - 894
  • [5] Ballistocardiogram Signal Denoising Using Independent Component Analysis
    Manjula, B. M.
    Sharma, Chirag
    [J]. ADVANCES IN SYSTEMS, CONTROL AND AUTOMATION, 2018, 442 : 259 - 267
  • [6] A Novel Algorithm for Independent Component Analysis with Reference and Methods for Its Applications
    Mi, Jian-Xun
    [J]. PLOS ONE, 2014, 9 (05):
  • [7] A comparative study of independent component analysis algorithms for electroencephalography
    Mutihac, Radu
    Mutihac, Radu Cristian
    [J]. ROMANIAN REPORTS IN PHYSICS, 2007, 59 (03) : 831 - 860
  • [8] Comparative study of VLSI solutions to independent component analysis
    Du, Hongtao
    Qi, Hairong
    Wang, Xiaoling
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2007, 54 (01) : 548 - 558
  • [9] An Improved Independent component analysis with reference
    Jia, Yanfei
    Yang, Xiaodong
    Xu, Liyue
    Zhao, Liquan
    [J]. ADVANCES IN COMPUTERS, ELECTRONICS AND MECHATRONICS, 2014, 667 : 64 - +
  • [10] EEG SIGNAL CLASSIFICATION USING NONLINEAR INDEPENDENT COMPONENT ANALYSIS
    Oveisi, Farid
    [J]. 2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS, 2009, : 361 - 364
12345