Independent Component Analysis and Blind Signal Separation: by Zhijian Yuan, Erkki Oja (auth.), Carlos G. Puntonet, Alberto

By Zhijian Yuan, Erkki Oja (auth.), Carlos G. Puntonet, Alberto Prieto (eds.)

This ebook constitutes the refereed complaints of the fifth overseas convention on self sufficient part research and Blind resource Separation, ICA 2004, held in Granada, Spain, in September 2004.

The 156 revised papers offered have been conscientiously reviewed and chosen from 203 submissions. The papers are equipped in topical sections on idea and foundations, linear types, covolutive types, nonlinear versions, speech processing purposes, photo processing functions, biomedical functions, and different applications.

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Additional info for Independent Component Analysis and Blind Signal Separation: Fifth International Conference, ICA 2004, Granada, Spain, September 22-24, 2004. Proceedings

Example text

KL (x), that is: L ϕˆi (x) = wi ki (x) = k(x)T w (26) i=1 and w is the minimizer of E (ϕi (x) − ϕˆi (x))2 . Following similar calculation as above, we obtain the same algorithm given by equations (22), (23) and (24), where in this case: ξ= 1 E ϕˆi (x)2 − E 2 ∂ ϕˆi (x) ∂xi (27) Finally, SFD is estimated by calculating the difference of the estimated MSF and JSF. Blind Source Separation by Adaptive Estimation 7 15 Normalization of Output Energies From the scale indeterminacy it is deducted that the algorithm (12) has no restriction on the energy of outputs.

Interested readers are referred to the references mentioned above and the references therein. In this paper, we will focus on a special type of homomorphic transformation, called the Gaussianizing function. Several interesting observations about this transformation and its utility in ICA will be addressed in this paper. Especially, we will establish a link between a Gaussianizing function based topology for solving linear instantaneous mixture problems and the established technique of nonlinear principal components analysis (NPCA) [6], which has already been shown to encompass a number of linear ICA optimization criteria as special cases [1] corresponding to certain choices of the nonlinear functions of projection.

SNR is defined as: SNR = 10 log10 E s2 E {(y − s)2 } (30) where y is the output corresponding to the source s. The figure shows that the proposed algorithm has better separation performance than EASI, as was expected because this algorithm is an optimal version of EASI (see Section 4). However the cost of this better performance is a higher complexity (which increases with the source number), since a multivariate non-linear function must be estimated at each iteration. 16 Samareh Samadi et al. Fig.

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