Abstract
SOBI is a blind source separation algorithm based on time decorrelation. It uses multiple time autocovariance matrices, and performs joint diagonalization thus being more robust than previous time decorrelation algorithms such as AMUSE. We propose an extensioncalled mdSOBI by using multidimensional autocovariances, which can be calculated for data sets with multidimensional parameterizations such as images or fMRI scans. mdSOBI has the advantage of using the spatial data in all directions, whereas SOBI only uses a single direction. These findings are confirmed by simulations and an application to fMRI analysis, where mdSOBI outperforms SOBI considerably.
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References
Cichocki, A., Amari, S.: Adaptive blind signal and image processing. John Wiley & Sons, Chichester (2002)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent component analysis. John Wiley & Sons, Chichester (2001)
Tong, L., Liu, R.W., Soon, V., Huang, Y.F.: Indeterminacy and identifiability of blind identification. IEEE Transactions on Circuits and Systems 38, 499–509 (1991)
Belouchrani, A., Meraim, K.A., Cardoso, J.F., Moulines, E.: A blind source separation technique based on second order statistics. IEEE Transactions on Signal Processing 45, 434–444 (1997)
Ziehe, A., Mueller, K.R.: TDSEP – an efficient algorithm for blind separation using time structure. In: Niklasson, L., Bodén, M., Ziemke, T. (eds.) Proc. of ICANN 1998, Skövde, Sweden, pp. 675–680. Springer, Berlin (1998)
Joho, M., Mathis, H., Lamber, R.: Overdetermined blind source separation: using more sensors than source signals in a noisy mixture. In: Proc. of ICA 2000, Helsinki, Finland, pp. 81–86 (2000)
Cardoso, J.F., Souloumiac, A.: Jacobi angles for simultaneous diagonalization. SIAM J. Mat. Anal. Appl. 17, 161–164 (1995)
Yeredor, A.: Non-orthogonal joint diagonalization in the leastsquares sense with application in blind source separation. IEEE Trans. Signal Processing 50, 1545–1553 (2002)
Ziehe, A., Laskov, P., Mueller, K.R., Nolte, G.: A linear least-squares algorithm for joint diagonalization. In: Proc. of ICA 2000, Nara, Japan, pp. 469–474 (2003)
Cardoso, J.F., Souloumiac, A.: Blind beamforming for non gaussian signals. IEE Proceedings - F 140, 362–370 (1993)
Woods, R., Cherry, S., Mazziotta, J.: Rapid automated algorithm for aligning and reslicing pet images. Journal of Computer Assisted Tomography 16, 620–633 (1992)
McKeown, M., Jung, T., Makeig, S., Brown, G., Kindermann, S., Bell, A., Sejnowksi, T.: Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping 6, 160–188 (1998)
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© 2004 Springer-Verlag Berlin Heidelberg
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Theis, F.J., Meyer-Bäse, A., Lang, E.W. (2004). Second-Order Blind Source Separation Based on Multi-dimensional Autocovariances. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_92
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DOI: https://doi.org/10.1007/978-3-540-30110-3_92
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