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Volumn 6, Issue , 2000, Pages 3426-3429

Local stability analysis of flexible independent component analysis algorithm

Author keywords

[No Author keywords available]

Indexed keywords

BLIND SOURCE SEPARATION; GAUSSIAN DISTRIBUTION; INDEPENDENT COMPONENT ANALYSIS; ASYMPTOTIC STABILITY; DIGITAL FILTERS; LEARNING ALGORITHMS; MATHEMATICAL MODELS; MATHEMATICAL OPERATORS; MATRIX ALGEBRA; OPTIMIZATION; PARAMETER ESTIMATION; PROBABILITY DISTRIBUTIONS; STATISTICAL METHODS; VECTORS;

EID: 0033694275     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2000.860137     Document Type: Conference Paper
Times cited : (6)

References (11)
  • 1
    • 0033570817 scopus 로고    scopus 로고
    • Natural gradient for over- and undercomplete bases in ICA
    • Nov.
    • S. Amari. Natural gradient for over- and undercomplete bases in ICA. Neural Computation, 11(8):1875-1883, Nov. 1999.
    • (1999) Neural Computation , vol.11 , Issue.8 , pp. 1875-1883
    • Amari, S.1
  • 2
    • 0031277419 scopus 로고    scopus 로고
    • Stability analysis of learning algorithms for blind source separation
    • S. Amari, T. P. Chen, and A. Cichocki. Stability analysis of learning algorithms for blind source separation. Neural Networks, 10(8):1345-1351, 1997.
    • (1997) Neural Networks , vol.10 , Issue.8 , pp. 1345-1351
    • Amari, S.1    Chen, T.P.2    Cichocki, A.3
  • 3
    • 0029411030 scopus 로고
    • An information maximisation approach to blind separation and blind deconvolution
    • A. Bell and T. Sejnowski. An information maximisation approach to blind separation and blind deconvolution. Neural Computation, 7:1129-1159, 1995.
    • (1995) Neural Computation , vol.7 , pp. 1129-1159
    • Bell, A.1    Sejnowski, T.2
  • 5
    • 0030417779 scopus 로고    scopus 로고
    • Equivariant adaptive source separation
    • Dec.
    • J.-F. Cardoso and B. H. Laheld. Equivariant adaptive source separation. IEEE Trans. Signal Processing, 44(12):3017-3030, Dec. 1996.
    • (1996) IEEE Trans. Signal Processing , vol.44 , Issue.12 , pp. 3017-3030
    • Cardoso, J.-F.1    Laheld, B.H.2
  • 6
    • 0031634640 scopus 로고    scopus 로고
    • Flexible independent component analysis
    • T. Constantinides, S. Y. Kung, M. Niranjan, and E. Wilson, editors
    • S. Choi, A. Cichocki, and S. Amari. Flexible independent component analysis. In T. Constantinides, S. Y. Kung, M. Niranjan, and E. Wilson, editors, Neural Networks for Signal Processing VIII, pages 83-92, 1998.
    • (1998) Neural Networks for Signal Processing VIII , pp. 83-92
    • Choi, S.1    Cichocki, A.2    Amari, S.3
  • 8
    • 0028416938 scopus 로고
    • Independent component analysis, a new concept?
    • P. Comon. Independent component analysis, a new concept? Signal Processing, 36(3):287-314, 1994.
    • (1994) Signal Processing , vol.36 , Issue.3 , pp. 287-314
    • Comon, P.1
  • 9
    • 0031335257 scopus 로고    scopus 로고
    • Multichannel blind separation and deconvolution of sources with arbitrary distributions
    • J. Principe, L. Gile, N. Morgan, and E. Wilson, editors
    • S. C.. Douglas, A. Cichocki, and S. Amari. Multichannel blind separation and deconvolution of sources with arbitrary distributions. In J. Principe, L. Gile, N. Morgan, and E. Wilson, editors, Neural Networks for Signal Processing VII, pages 436-445, 1997.
    • (1997) Neural Networks for Signal Processing VII , pp. 436-445
    • Douglas, S.C.1    Cichocki, A.2    Amari, S.3
  • 10
    • 0000324990 scopus 로고    scopus 로고
    • An alternative perspective on adaptive independent component analysis algorithms
    • Nov.
    • M. Girolami. An alternative perspective on adaptive independent component analysis algorithms. Neural Computation, 10(8):2103-2114, Nov. 1998.
    • (1998) Neural Computation , vol.10 , Issue.8 , pp. 2103-2114
    • Girolami, M.1
  • 11
    • 0033556834 scopus 로고    scopus 로고
    • Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources
    • T. W. Lee, M. Girolami, and T. Sejnowski. Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources. Neural Computation, 11(2):609-633, 1999.
    • (1999) Neural Computation , vol.11 , Issue.2 , pp. 609-633
    • Lee, T.W.1    Girolami, M.2    Sejnowski, T.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.