Journal of the Optical Society of Korea

Optical Society of Korea (한국광학회)
권호 표지
DOI QR코드

DOI QR Code

Sparse Kernel Independent Component Analysis for Blind Source Separation

  • Khan, Asif (Signal Processing Lab, Department of Communication Engineering Myongji University) ;
  • Kim, In-Taek (Signal Processing Lab, Department of Communication Engineering Myongji University)
  • Received : 2008.08.04
  • Accepted : 2008.09.02
  • Published : 2008.09.25
Download PDF
⟨ Previous Next ⟩

Abstract

We address the problem of Blind Source Separation(BSS) of superimposed signals in situations where one signal has constant or slowly varying intensities at some consecutive locations and at the corresponding locations the other signal has highly varying intensities. Independent Component Analysis(ICA) is a major technique for Blind Source Separation and the existing ICA algorithms fail to estimate the original intensities in the stated situation. We combine the advantages of existing sparse methods and Kernel ICA in our technique, by proposing wavelet packet based sparse decomposition of signals prior to the application of Kernel ICA. Simulations and experimental results illustrate the effectiveness and accuracy of the proposed approach. The approach is general in the way that it can be tailored and applied to a wide range of BSS problems concerning one-dimensional signals and images(two-dimensional signals).

Keywords

References

  1. J. Herault and C. Jutten, "Space or time adaptive signal processing by neural models," In Proc AIP Conf on Neural Networks for Computing, American Institute of Physics , pp. 206–211, 1986
  2. R. Linsker, "An application of the principle of maximum information preservation to linear systems," Advances in neural information processing systems, Morgan Kaufmann, San Francisco, CA, pp. 186–194, 1989
  3. A. J. Bell and T. J. Sejnowski, "An information- maximization approach to blind separation and blind deconvolution," Neural Comput, vol. 7, pp. 1129–1159, 1995 https://doi.org/10.1162/neco.1995.7.6.1129
  4. S. Amari, A. Cichocki, and H.H. Yang, "A new learning algorithm for blind signal separation," Advances in neural information processing systems, vol. 8, MIT Press, Cambridge, MA, 1996
  5. M. Zibulevsky and B.A. Pearlmutter, "Blind source separation by sparse decomposition in a signal dictionary," Neural Comput, vol. 13, pp. 863–882, 2001 https://doi.org/10.1162/089976601300014385
  6. F. R. Bach and M. I. Jordan, "Kernel Independent Component Analysis," Journal of Machine Learning Research, vol. 3, pp. 1-48, 2002 https://doi.org/10.1162/153244303768966085
  7. B. Scholkopf and A. Smola,"Learning with Kernels," (Cambridge, Mass, MIT Press, 2002)
  8. Michael M. Broristeiti, Alexander M. Bronstein, Michael Zibirlevshy, and Yehoshzta Y. Zeevi, "Separation of Reflections via Sparse ICA," International Journal of Imaging Systems and Technology, vol. 15, no. 1, pp. 84-91, 2005 https://doi.org/10.1002/ima.20042
  9. Paul D. O'Grady, Barak A. Pearlmutter, and Scott T. Rickard, "Survey of Sparse and Non-Sparse Methods in Source Separation," International Journal of Imaging Systems and Technology, vol. 15, no. 1, pp. 18-33, 2005 https://doi.org/10.1002/ima.20035
  10. P. Kisilev, M. Zibulevsky, and Y.Y. Zeevi. "Multiscale framework for blind source separation," JMLR, vol. 4, pp. 1339-1364, 2004 https://doi.org/10.1162/jmlr.2003.4.7-8.1339

Cited by

  1. Illumination normalization using independent component analysis and filtering vol.65, pp.5, 2017, https://doi.org/10.1080/13682199.2017.1338815
  2. Functional brain networks reconstruction using group sparsity-regularized learning 2017, https://doi.org/10.1007/s11682-017-9737-4