Abstract
Blind source extraction (BSE) is of advantages over blind source separation (BSS) when obtaining some underlying source signals from high dimensional observed signals. Among a variety of BSE algorithms, a large number of algorithms are based on linear prediction (LP-BSE). In this paper we analyze them from practical point of view. We reveal that they are, in nature, minor component analysis (MCA) algorithms, and thus they have some problems that are inherent in MCA algorithms. We also find a switch phenomenon of online LP-BSE algorithms, showing that different parts of a single extracted signal are the counterparts of different source signals. The two issues should be noticed when one applies these algorithms to practical applications. Computer simulations are given to confirm these observations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. John Wiley & Sons, New York (2002)
Cichocki, A., et al.: A Blind Extraction of Temporally Correlated but Statistically Dependent Acoustic Signals. In: Proc. of the 2000 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing X, pp. 455–464. IEEE Computer Society Press, Los Alamitos (2000)
Smith, D., Lukasiak, J., Burnett, I.: Blind Speech Separation Using a Joint Model of Speech Production. IEEE Signal Processing Lett. 12(11), 784–787 (2005)
Zhang, Z.-L., Yi, Z.: Robust Extraction of Specific Signals with Temporal Structure. Neurocomputing 69(7-9), 888–893 (2006)
Barros, A.K., Cichocki, A.: Extraction of Specific Signals with Temporal Structure. Neural Computation 13(9), 1995–2003 (2001)
Cichocki, A., Thawonmas, R.: On-line Algorithm for Blind Signal Extraction of Arbitrarily Distributed, but Temporally Correlated Sources Using Second Order Statistics. Neural Processing Letters 12, 91–98 (2000)
Mandic, D.P., Cichocki, A.: An Online Algorithm for Blind Extraction of Sources with Different Dynamical Structures. In: Proc. of the 4th Int. Conf. on Independent Component Analysis and Blind Signal Separation (ICA 2003), pp. 645–650 (2003)
Liu, W., Mandic, D.P., Cichocki, A.: A Class of Novel Blind Source Extraction Algorithms Based on a Linear Predictor. In: Proc. of ISCAS 2005, pp. 3599–3602 (2005)
Liu, W., Mandic, D.P., Cichocki, A.: Blind Second-order Source Extraction of Instantaneous Noisy Mixtures. IEEE Trans. Circuits Syst. II 53(9), 931–935 (2006)
Taleb, A., Cirrincione, G.: Against the Convergence of the Minor Component Analysis Neurons. IEEE Trans. Neural Networks 10(1), 207–210 (1999)
Feng, D.-Z., Zheng, W.-X., Jia, Y.: Neural Network Learning Algorithms for Tracking Minor Subspace in High-dimensional Data Stream. IEEE Trans. Neural Networks 16(3), 513–521 (2005)
Wilkinson, J.H.: The Algebraic Eigenvalue Problem. Oxford Univ. Press, Oxford (1965)
Makeig, S., Westerfield, M., Jung, T.-P., et al.: Dynamic Brain Sources of Visual Evoked Responses. Science 295, 690–694 (2002)
Golub, G.H., Loan, C.F.V.: Matrix Computation, 3rd edn. The John Hopkins University Press, Baltimore (1996)
Zhang, Q., Leung, Y.-W.: A Class of Learning Algorithms for Principal Component Analysis and Minor Component Analysis. IEEE Trans. Neural Networks 11(1), 200–204 (2000)
Chatterjee, C.: Adaptive Algorithms for First Principal Eigenvector Computation. Neural Networks 18, 145–159 (2005)
ICALAB Toolboxes, Available: http://www.bsp.brain.riken.jp/ICALAB
Vigário, R., et al.: Independent Component Analysis for Identification of Artifacts in Magnetoencephalographic Recordings. In: Proc. of NIPS 1997, pp. 229–235 (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, ZL., Zhang, L. (2007). Linear Prediction Based Blind Source Extraction Algorithms in Practical Applications. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_39
Download citation
DOI: https://doi.org/10.1007/978-3-540-74494-8_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74493-1
Online ISBN: 978-3-540-74494-8
eBook Packages: Computer ScienceComputer Science (R0)