Abstract
Blind source extraction (BSE) is particularly attractive to solve blind signal mixture problems where only a few source signals are desired. Many existing BSE methods do not take into account the existence of noise and can only work well in noise-free environments. In practice, the desired signal is often contaminated by additional noise. Therefore, we try to tackle the problem of noisy component extraction. The reference signal carries enough prior information to distinguish the desired signal from signal mixtures. According to the useful properties of Gaussian moments, we incorporate the reference signal into a negentropy objective function so as to guide the extraction process and develop an improved BSE method. Extensive computer simulations demonstrate its validity in the process of revealing the underlying desired signal.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Cichocki A, Amari S. Adaptive blind signal and image processing. New York: Wiley, 2003
Hyvärinen A, Karhunen J, Oja E. Independent component analysis. New York: Wiley, 2001
Zhao Y J, Liu B Q, Wang S. A robust extraction algorithm for biomedical signals from noisy mixtures. Frontiers of Computer Science in China, 2011, 5(4): 387–394
Barros A K, Cichocki A. Extraction of specific signals with temporal structure. Neural Computation, 2001, 13(9): 1995–2003
Santata E, Principe J C, Santana E E. Extraction of signals with specific temporal structure using kernel methods. IEEE Transactions on Signal Processing, 2010, 58(10): 5142–5150
Leong W Y, Mandic D P. Noisy component extraction (NoiCE). IEEE Transactions on Circuits and Systems, 2010, 57(3): 664–671
Lu W, Rajapakse J C. ICA with reference. Neurocomputing, 2006, 69(16–18): 2244–2257
Lu W, Rajapakse J C. Approach and applications of constrained ICA. IEEE Transactions on Neural Networks, 2005, 16(1): 203–212
Huang D S, Mi J X. A new constrained independent component analysis method. IEEE Transactions on Neural Networks, 2007, 18(5): 1532–1535
Lin Q H, Zheng Y R, Yin F L. A fast algorithm for one-unit ICA-R. Information Science, 2007, 177: 1265–1275
James C J, Gibson O J. Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis. IEEE Transactions on Biomedical Engineering, 2003, 50(9): 1108–1116
Zhang Z L. Morphologically constrained ICA for extracting weak temporally correlated signals. Neurocomputing, 2008, 71(7–9): 1669–1679
Hyvärinen A. Gaussian moments for noisy independent component analysis. IEEE Signal Processing Letters, 1999, 6(6): 145–147
James C J, Hesse CW. Independent component analysis for biomedical signals. Physiological Measurement, 2005, 26(1): 15–39
Liu W, Mandic D P. A normalized kurtosis-based algorithm for blind source extraction from noisy measurements. Signal Processing, 2006, 86(7): 1580–1585
Author information
Authors and Affiliations
Corresponding author
Additional information
Yongjian Zhao received his BSc from East China University of Science and Technology, Shanghai, China, in 1991, and his PhD in Biomedical Engineering from Shandong University, China, in 2012. He is currently an associate professor in the Department of Computer Science, Shandong University, Weihai. He has authored more than 20 research publications in refereed international journals and international conference proceedings. His research interests include biomedical signal processing, blind source separation, and pattern recognition.
Hong He received her PhD in computer software and theory from Shandong University, Jinan, China, in 2002. She is currently an associate professor in Shandong University, Weihai. Her research interests are in the areas of algorithm analysis and design, and Internet-based computing.
Jianxun Mi received his BSc in automation from Sichuan University, Chengdu, China in 2004 and his PhD in pattern recognition and intelligent systems from the University of Science and Technology of China, Hefei, China, in 2010. He joined the Bio-Computing Research Center at Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China as a postdoctoral research fellow in 2011.
Rights and permissions
About this article
Cite this article
Zhao, Y., He, H. & Mi, J. Noisy component extraction with reference. Front. Comput. Sci. 7, 135–144 (2013). https://doi.org/10.1007/s11704-013-1135-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11704-013-1135-5