Abstract
Presented here is a generalization of the modified relative Newton method, recently proposed in [1] for quasi-maximum likelihood blind source separation. Special structure of the Hessian matrix allows to perform block-coordinate Newton descent, which significantly reduces the algorithm computational complexity and boosts its performance. Simulations based on artificial and real data show that the separation quality using the proposed algorithm outperforms other accepted blind source separation methods.
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References
Zibulevsky, M.: Sparse source separation with relative Newton method. In: Proc. ICA 2003, pp. 897–902 (2003)
Zibulevsky, M., Pearlmutter, B.A.: Blind source separation by sparse decomposition in a signal dictionary. Neural Comp 13, 863–882 (2001)
Zibulevsky, M., Pearlmutter, B.A., Bofill, P., Kisilev, P.: Blind source separation by sparse decomposition. In: Roberts, S.J., Everson, R.M. (eds.) Independent Components Analysis: Principles and Practice, Cambridge University Press, Cambridge (2001)
Pham, D., Garrat, P.: Blind separation of a mixture of independent sources through a quasimaximum likelihood approach. IEEE Trans. Sig. Proc. 45, 1712–1725 (1997)
Bell, A.J., Sejnowski, T.J.: An information maximization approach to blind separation and blind deconvolution. Neural Comp. 7, 1129–1159 (1995)
Amari, S., A.C., Yang, H.H.: A new learning algorithm for blind signal separation. Advances in Neural Information Processing Systems 8 (1996)
Cichocki, A., Unbehauen, R., Rummert, E.: Robust learning algorithm for blind separation of signals. Electronics Letters 30, 1386–1387 (1994)
Grippo, L., Sciandrone, M.: Globally convergent block-coordinate techniques for unconstrained optimization. Optimization Methods and Software 10, 587–637 (1999)
Bronstein, A.M., Bronstein, M.M., Zibulevsky, M.: Block-coordinate relative Newton method for blind source separation. Technical Report 445, Technion, Israel (2003)
Bofill, P., Zibulevsky, M.: Underdetermined blind source separation using sparse representations. Sig. Proc. 81, 2353–2362 (2001)
Bronstein, A.M., Bronstein, M.M., Zibulevsky, M., Zeevi, Y.Y.: Separation of reflections via sparse ICA. In: Proc. IEEE ICIP (2003)
Makeig, S.: ICA toolbox for psychophysiological research (1998), Online http://www.cnl.salk.edu/~ica.html
Hyvärinen, A.: The Fast-ICA MATLAB package (1998), Online http://www.cis.hut.fi/~aapo
Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Net. 10, 626–634 (1999)
Cardoso, J.F.: JADE for real-valued data (1999), Online http://sig.enst.fr:80/~cardoso/guidesepsou.html
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© 2004 Springer-Verlag Berlin Heidelberg
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Bronstein, A.M., Bronstein, M.M., Zibulevsky, M. (2004). Blind Source Separation Using the Block-Coordinate Relative Newton Method. 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_52
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DOI: https://doi.org/10.1007/978-3-540-30110-3_52
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