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Volumn 25, Issue 2, 2007, Pages 91-110

Robust prewhitening for ICA by minimizing β-divergence and its application to FastICA

Author keywords

prewhitening; Adaptive selection; Independent component analysis; One standard error; Robustness

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; MEASUREMENT THEORY; PARAMETER ESTIMATION; SIGNAL PROCESSING; TUNING; WHITE NOISE;

EID: 33847314660     PISSN: 13704621     EISSN: 1573773X     Source Type: Journal    
DOI: 10.1007/s11063-006-9023-8     Document Type: Article
Times cited : (42)

References (15)
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    • Belouchrani, A.1    Cichocki, A.2
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    • Fast and robust fixed-point algorithms for independent component analysis
    • Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis, IEEE Trans. on Neural Network 10(3) (1999), 626-34.
    • (1999) IEEE Trans. on Neural Network , vol.10 , Issue.3 , pp. 626-634
    • Hyvärinen, A.1
  • 11
    • 0346307721 scopus 로고    scopus 로고
    • A fast fixed-point algorithm for independent component analysis
    • Hyvärinen, A. and Oja, E.: A fast fixed-point algorithm for independent component analysis, Neural Computation 9(7) (1997), 1483-1492.
    • (1997) Neural Computation , vol.9 , Issue.7 , pp. 1483-1492
    • Hyvärinen, A.1    Oja, E.2
  • 13
    • 0040673441 scopus 로고    scopus 로고
    • Robust Blind Source Separation by beta-Divergence
    • Minami, M. and Eguchi, S.: Robust Blind Source Separation by beta-Divergence, Neural Computation 14 (2002), 1859-1886.
    • (2002) Neural Computation , vol.14 , pp. 1859-1886
    • Minami, M.1    Eguchi, S.2
  • 15
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    • Exploring Latent Structure of Mixture ICA Models by the Minimum β-Divergence Method
    • Mollah, M. N. H., Minami, M. and Eguchi, S.: Exploring Latent Structure of Mixture ICA Models by the Minimum β-Divergence Method, Neural Computation 18(1) (2006), 166-190.
    • (2006) Neural Computation , vol.18 , Issue.1 , pp. 166-190
    • Mollah, M.N.H.1    Minami, M.2    Eguchi, S.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.