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Volumn 18, Issue 1, 2006, Pages 166-190

Exploring latent structure of mixture ICA models by the minimum β-divergence method

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EID: 33645716208     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/089976606774841549     Document Type: Article
Times cited : (31)

References (16)
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    • Amari, S.1    Cardoso, J.F.2
  • 2
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    • A new learning algorithm for blind source separation
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    • Amari, S., Cichocki, A., & Yang, H. H. (1996). A new learning algorithm for blind source separation. In D. Touretzky, M. Mozer, & M. Hasselm. (Eds.), Advances in neural information processing, 8, (pp. 757-763). Cambridge, MA: MIT Press.
    • (1996) Advances in Neural Information Processing , vol.8 , pp. 757-763
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  • 3
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    • An information-maximization approach to blind separation and blind deconvolution
    • Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7, 1129-1159.
    • (1995) Neural Computation , vol.7 , pp. 1129-1159
    • Bell, A.J.1    Sejnowski, T.J.2
  • 4
    • 0027812550 scopus 로고
    • Blind beamforming for non-gaussian signals
    • Cardoso, J. F., & Souloumiac, A. (1993). Blind beamforming for non-gaussian signals. Proc. IEEE, 140, 362-470.
    • (1993) Proc. IEEE , vol.140 , pp. 362-470
    • Cardoso, J.F.1    Souloumiac, A.2
  • 8
    • 0026191274 scopus 로고
    • Blind separation of sources, Part I: An adaptive algorithm based on neuromimetic architecture
    • Jutten, C., & Hérault, J. (1991). Blind separation of sources, Part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing, 24, 1-20.
    • (1991) Signal Processing , vol.24 , pp. 1-20
    • Jutten, C.1    Hérault, J.2
  • 10
    • 0001144814 scopus 로고    scopus 로고
    • Independent component analysis using an extended infomax algorithm for mixed sub-gaussian and super-gaussian sources
    • Lee, T.-W., Girolami, M., & Sejnowski, T. J. (1999). Independent component analysis using an extended infomax algorithm for mixed sub-gaussian and super-gaussian sources. Neural Computation, 14, 409-433.
    • (1999) Neural Computation , vol.14 , pp. 409-433
    • Lee, T.-W.1    Girolami, M.2    Sejnowski, T.J.3
  • 12
    • 0034290916 scopus 로고    scopus 로고
    • ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation
    • Lee, T.-W., Lewicki, M. S., & Sejnowski, T. J. (2000). ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation. IEEE Trans. on Pattern Analysis an Machine Int., 22, 1078-1089.
    • (2000) IEEE Trans. on Pattern Analysis An Machine Int. , vol.22 , pp. 1078-1089
    • Lee, T.-W.1    Lewicki, M.S.2    Sejnowski, T.J.3
  • 14
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    • Robust blind source separation by β-divergence
    • Minami, M., & Eguchi, S. (2002). Robust blind source separation by β-divergence. Neural Computation, 14, 1859-1886.
    • (2002) Neural Computation , vol.14 , pp. 1859-1886
    • Minami, M.1    Eguchi, S.2
  • 15
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    • Adaptive selection for minimum β-divergence method
    • Nara, Japan
    • Minami, M., & Eguchi, S. (2003). Adaptive selection for minimum β-divergence method. In Proceedings of ICA-2003 Conference. Nara, Japan.
    • (2003) Proceedings of ICA-2003 Conference
    • Minami, M.1    Eguchi, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.