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IEEE/ICACT20230070 Slide.09        [Big Slide]	Oral Presentation
	ICA separates signals by exploiting the non-Gaussianity of each signal. Negentropy is used as a measure of non-Gaussianity, which measures non-Gaussianity using entropy. In order to calculate the expected value, if the number of samples is small, it may not converge to the expected value due to the theorem of large numbers. Therefore, if the data series length is short, non-Gaussianity cannot be evaluated sufficiently, and convergence of 〖𝒘^¡Ç〗_𝑚 in equation (5-1) becomes difficult, and the accuracy of ICA may be reduced.

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