Abstract
The main goal of this paper is to review the fundamental ideas of the method called “ICA with OS”. We review a set of alternative statistical distances between distributions based on the Cumulative Density Function (cdf). In particular, these gaussianity distances provide new cost functions whose maximization perform the extraction of one independent component at each successive stage of a new proposed deflation ICA procedure. These measures are estimated through Order Statistics (OS) that are consistent estimators of the inverse cdf. The new Gaussianity measures improve the ICA performance and also increase the robustness against outliers compared with the traditional ones.
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References
Amari, S., Cichocki, A., Yang, H.H.: A New Learning Algorithm for Blind Signal Separation. In: Proc. of Neural Information Processing Systems, NIPS 1996, vol. 8, pp. 757–763 (1996)
Lee, T.-W.: Independent Component Analysis: Theory and Applications. Kluwer Academic Publishers, Dordrecht (1998)
Blanco, Y., Zazo, S., Páez-Borrallo, J.M.: Adaptive Processing of Blind Source Separation through ’ICA with OS’. In: Proceedings of the International Conference On Acoustics And Signal Processing ICASSP 2000, vol. I, pp. 233–236 S (2000)
Blanco, Y., Zazo, S., Principe, J.C.: Alternative Statistical Gaussianity Measure using the Cumulative Density Function. In: Proceedings of the Second Workshop on Independent Component Analysis and Blind Signal Separation: ICA 2000. pp. 537–542 (2000)
Blanco, Y., Zazo, S., Páez-Borrallo, J.M.: Adaptive ICA with Order Statistics: An efficient approach based on serial orthogonal projections. In: Mira, J., Prieto, A.G. (eds.) IWANN 2001. LNCS, vol. 2085, pp. 770–778. Springer, Heidelberg (2001)
Blanco, Y., Zazo, S.: New Gaussianity Measures based on Order Statistics. Application to ICA. Elsevier. Neurocomputing 51, 303–320 (2003)
Cardoso, J.F., Soulimiac, A.: Blind beamforming for Non Gaussian Signals. IEE Proceedings-F 140(6), 362–370 (1993)
Common, P.: Independent Component Analysis, A New Concept. Signal Processing (36), 287–314 (1992)
Hyvärien, A., Karhunen, J., Oja, E.: Independent Component Analysis. Ed. John Wiley & sons, Chichester (2001)
Kung, S.Y., Mejuto, C.: Extraction of Independent Components from Hybrid Mixture: Knicnet Learning Algorithm and Applications. In: Proc. International Conference On Acoustics And Signal Processing ICASSP 1998, vol. II, pp. 1209,1211 (1998)
Papoulis, A.: Probability and Statistics. Prentice Hall International, INC, Englewood Cliffs (1999)
Learned-Miller, E.G.: ICA Using Spacing Estimates of Entropy. Journal of Machine Learning Research 4, 1271–1295 (2003)
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Blanco, Y., Zazo, S. (2004). An Overview of BSS Techniques Based on Order Statistics: Formulation and Implementation Issues. 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_10
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DOI: https://doi.org/10.1007/978-3-540-30110-3_10
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