Multiset Canonical Correlation Analysis Using for Blind Source Separation


Article Preview

To solve the problem of blind source separation, a novel algorithm based on multiset canonical correlation analysis is presented by exploiting the different temporal structure of uncorrelated source signals. In contrast to higher order cumulant techniques, this algorithm is based on second order statistical characteristic of observation signals, can blind separate super-Gaussian and sub-Gaussian signals successfully at the same time with relatively light computation burden. Simulation results confirm that the algorithm is efficient and feasible.



Edited by:

J.W. Hu and J. Su






H. G. Yu et al., "Multiset Canonical Correlation Analysis Using for Blind Source Separation", Applied Mechanics and Materials, Vols. 195-196, pp. 104-108, 2012

Online since:

August 2012




[1] L. Albera, A. Ferréol, P. Comon and P. Chevalier, Blind identification of overcomplete mixtures of sources (BIOME), Linear Alg. Appl., vol. 391, p.1–30, Nov. (2004).

DOI: 10.1016/j.laa.2004.05.007

[2] H. Hotelling, Relations between two sets of variates. Biometrika, vol. 28, p.312–377, (1936).

DOI: 10.2307/2333955

[3] D. R. Hardoon, S. Szedmak and J. Shawe-Taylor, Canonical correlation analysis: an overview with application to learning methods, Neural Computation, vol. 16, p.2639–2664, (2004).

DOI: 10.1162/0899766042321814

[4] S. Y. Huang, M. H. Lee and C. K. Hsiao, Nonlinear measures of association with kernel canonical correlation analysis and applications, Journal of Statistical Planning and Inference, vol. 139, p.2162–2174, (2009).

DOI: 10.1016/j.jspi.2008.10.011

[5] J. Via, I. Santamaria, and J. Perez, Deterministic CCA-Based Algorithms for Blind Equalization of FIR-MIMO Channels, IEEE Transactions on Signal Processing, vol. 55, pp.3867-3878, (2007).

DOI: 10.1109/tsp.2007.894273

[6] W. Liu, D. P. Mandic, and A. Cichocki, Analysis and Online Realization of the CCA Approach for Blind Source Separation, IEEE Transactions on Neural Networks, vol. 18, pp.1505-1510, (2007).

DOI: 10.1109/tnn.2007.894017

[7] Y. -O Li, T. Adali, W. Wang, and V. D. Calhoun, Joint blind source separation by multiset canonical correlation analysis, IEEE Transactions on Signal Processing, vol. 57, pp.3918-3929, (2009).

DOI: 10.1109/tsp.2009.2021636

[8] M. Borga and H. Knutsson, A canonical correlation approach to blind source separation, Dept. Biomed. Eng., Linkoping Univ., Linkoping, Sweden, Tech. Rep. LiU-IMT-EX-0062, Jun. (2001).

[9] J. Kettenring, Canonical analysis of several sets of variables, Biometrika, vol. 58, pp.433-451, (1971).

DOI: 10.2307/2334380

[10] A. Ziehe, P. Laskov, G. Nolte, and K. -R. Múller, A fast algorithm for joint diagonalization with non-orthogonal transformations and its application to blind source separation, J. Mach. Learn. Res., vol. 5, p.777–800, (2004).

[11] S. Degerine and E. Kane, A Comparative Study of Approximate Joint Diagonalization Algorithms for Blind Source Separation in Presence of Additive Noise, IEEE Transactions on Signal Processing, vol. 55 pp.3022-3031, (2007).

DOI: 10.1109/tsp.2007.893974

[12] Cichocki. and S. Amari, Adaptive blind signal and image processing. New York, USA: Wiley, (2002).

In order to see related information, you need to Login.