Bridging a Gap in Independent Component Analysis

Article Preview

Abstract:

Independent Component Analysis (ICA) is a powerful method which aims at representing a given random signal as a sum of independent sources. The engineering community, however, works at the distribution function or characteristic function level while makes assertions at the random variable level. This legitimacy of this jump has never been established and consists of a longstanding gap in the ICA literature. In this paper, it is proved that existence of a factorization of characteristic function does imply existence of a corresponding decomposition of random variable into independent sum and thus the gap is bridged. The proof relies on two nontrivial results from probability theory.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1279-1283

Citation:

Online since:

December 2012

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] A. Hyvärinen, J. Karhunen, and E. Ojá: Independent Component Analysis (John Wiley & Sons, 2001).

Google Scholar

[2] A.J. Bell and T.J. Sejnowski: Vision Res. Vol 37 (1997), pp.3327-3338

Google Scholar

[3] J.H. van Hateren and A. van der Schaaf: Proc. Royal Soc. London, Ser. B Vol 265 (1998), pp.359-366

Google Scholar

[4] B.A. Olshausen and D. Field: Nature Vol 381 (1996), p.607–609

Google Scholar

[5] D.J. Field: Phil. Trans. Royal Soc. London, Ser. A, Vol 357 (1999), pp.2527-2542

Google Scholar

[6] I. Daubechies, E. Roussos, S. Takerkart, M. Benharrosh, C. Golden, K. D'Ardenne, W. Richter, J.D. Cohen, and J. Haxby: PNAS Vol. 106 (2009), pp.10415-10422

DOI: 10.1073/pnas.0903525106

Google Scholar

[7] Y. Amit, D. Geman, and K. Wilder: IEEE Trans. Patt. Anal. Mach. Intell. Vol 19 (1997), p.1300–1305

Google Scholar

[8] R. Battiti: IEEE Trans. Neu. Net. Vol 5 (1995), pp.537-550

Google Scholar

[9] B. V. Bonnlander and A.S. Weigend, in: Proc. of the 1994 Symp. on Artificial Neu. Net. (ISANN 94) (1996), pp.42-50

Google Scholar

[10] K. Torkkola: J. Mach. Learning Res. Vol 3 (2003), p.1415–1438

Google Scholar

[11] M. Vidal-Naquet and S. Ullman: in: Proc. Int. Conf. Comp. Vision 2003 (2003), p.281–288

Google Scholar

[12] F. Fleuret: J. Mach. Learning Res. Vol 5 (2004), p.1531–1555

Google Scholar

[13] M. Bressan and J. Vitrià: IEEE Trans. Patt. Anal. Mach. Intell. Vol. 25 (2003), pp.1312-1316

Google Scholar

[14] P. Comon: Sig. Proc. Vol. 36 (1994), pp.287-314 Special issue on higher-order statistics

Google Scholar

[15] A. Haddad and Y. Meyer: Variational Methods in Image Processing. Preprint CMLA 2005-08

Google Scholar

[16] J.-F. Cardoso's Homepage: http://sig.enst.fr/~cardoso/stuff.html

Google Scholar

[17] G.H. Dong and D.W. Hu. On Generic Impossibility of ICA Decomposition. (Submitted to IScIDE 2012)

Google Scholar

[18] W. Feller: An Introduction to Probability Theory and Its Applications, vol. 2 (John Wiley & Sons, 2nd ed., 1971)

Google Scholar

[19] A.M. Kagan, Yu.V. Linnik, and C. R. Rao: Characterization Problems in Mathematical Statistics (John Wiley & Sons, 1973)

Google Scholar

[20] Yu. V. Linnik: Decomposition of Probability Laws (Oliver and Boyd Ltd., Edinburgh, 1964)

Google Scholar

[21] E. Lucas: Characteristic Functions (Charles Griffin & Company Ltd., 1960)

Google Scholar

[22] G.M. Fel'dman: Arithmetic of Probability Distributions and Characterization Problems on Abelian Groups. Translations of Mathematics Monographs, vol. 116 (Simeon Ivanov, ed.), (American Mathematical Society, Providence, RI, 1993)

DOI: 10.1090/mmono/116/03

Google Scholar

[23] K.R. Parthasarathy, R. Ranga Rao, and S.R.S. Varadhan: Trans. AMS Vol. 102 (1962), pp.200-217

Google Scholar

[24] M. Kac: Statistical Independence in Probability, Analysis and Number Theory. (Published by the Mathematical Association of America, and Distributed by John Wiley and Sons, Inc., 1959)

Google Scholar

[25] O. Kallenberg: Foundations of Modern Probability (Springer-Verlag, 1997)

Google Scholar

[26] R.M. Dudley: Ann. Math. Stat. Vol. 39 (1968), pp.1563-1572

Google Scholar

[27] R. M. Dudley: Real Analysis and Probability (Cambridge University Press, 2004)

Google Scholar

[28] S.T. Rachev: Probability Metrics and the Stability of Stochastic Models (John Wiley & Sons, 1991)

Google Scholar

[29] P. J. Huber: Robust Statistics (John Wiley & Sons, New York, 1981)

Google Scholar