Abstract
Most ICA algorithms are sensitive to outliers. Instead of robustifying existing algorithms by outlier rejection techniques, we show how a simple outlier index can be used directly to solve the ICA problem for super-Gaussian source signals. This ICA method is outlier-robust by construction and can be used for standard ICA as well as for over-complete ICA (i.e. more source signals than observed signals (mixtures)).
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
Zibulevsky, M., Pearlmutter, B.A.: Blind source separation by sparse decomposition in a signal dictionary. Neural Computation 13, 863–882 (2001)
Bofill, P., Zibulevsky, M.: Underdetermined blind source separation using sparse representations. Signal Processing 81, 2353–2362 (2001)
Lee, T.W., Lewicki, M., Girolami, M., Sejnowski, T.: Blind source separation of more sources than mixtures using overcomplete representations. IEEE Signal Process. Lett. 6, 78–90 (1999)
Harmeling, S., Dornhege, G., Tax, D., Meinecke, F., Müller, K.R.: From outliers to prototypes: ordering data. Technical report (2004)
Puntonet, C.G., Prieto, A., Jutten, C., Rodriguez-Alvarez, M., Ortega, J.: Separation of sources: A geometry-based procedure for reconstruction of n-valued signals. Signal Processing 46, 267–284 (1995)
Cardoso, J.F., Souloumiac, A.: Blind beamforming for non Gaussian signals. IEEE Proceedings-F 140, 362–370 (1993)
Hyvärinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Computation 9, 1483–1492 (1997)
Chen, S., Donoho, D., Saunders, M.: Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing 20, 33–61 (1998)
Meinecke, F., Ziehe, A., Kawanabe, M., Müller, K.R.: A resampling approach to estimate the stability of one-dimensional or multidimensional independent components. IEEE Transactions on Biomedical Engineering 49, 1514–1525 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meinecke, F.C., Harmeling, S., Müller, KR. (2004). Robust ICA for Super-Gaussian Sources. 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_28
Download citation
DOI: https://doi.org/10.1007/978-3-540-30110-3_28
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23056-4
Online ISBN: 978-3-540-30110-3
eBook Packages: Springer Book Archive