By Rainville E.D.
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Extra info for Special functions
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The minimum support and the minimum range criteria coincide only when all the involved signals have convex support, otherwise they differ [10]. In this paper, we retake the minimum support criterion and extend its role as contrast function for mixtures of complex source signals. The paper is organized as follows. In section 2 we present the signal model. Section 3 and section 4 detail some useful results and geometrical object definitions. Section 5 presents the complex version of the minimum support criterion and other extensions.
Multidimensional independent component analysis. In: Proc. ICASSP ’98. Seattle (1998) 7. : Independent component analysis, a new concept. Signal Processing 36(3), 287–314 (1994) 8. : Complex random vectors and ICA models: Identifiability, uniqueness and separability. IEEE Trans. on Information Theory 52(3), 1017–1029 (2006) 9. : Blind beamforming for non gaussian signals. In: IEEE- Proceedings-F. vol. 140, pp. 362–370 (1993) The Complex Version of the Minimum Support Criterion Sergio Cruces, Auxiliadora Sarmiento, and Iván Durán Dpto.
The sources are Imposing Independence Constraints in the CP Model 39 zero mean and have unit variance. The entries of the two other modes (A and B) are drawn from a zero-mean unit-variance gaussian distribution. We conduct Monte Carlo simulations consisting of 500 runs. We evaluate the performance of the different algorithms by means of the normalized Frobenius norm of the difference between the estimated and the real sources: errorC = ˆ F ||C − C|| ||C||F (26) In figure 2, we plot the mean value of the 500 simulations.