Is the general form of Renyi's entropy a contrast for source separation?

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Abstract

Renyi's entropy-based criterion has been proposed as an objective function for independent component analysis because of its relationship with Shannon's entropy and its computational advantages in specific cases. These criteria were suggested based on "convincing" experiments. However, there is no theoretical proof that globally maximizing those functions would lead to separate the sources; actually, this was implicitly conjectured. In this paper, the problem is tackled in a theoretical way; it is shown that globally maximizing the Renyi's entropy-based criterion, in its general form, does not necessarily provide the expected independent signals. The contrast function property of the corresponding criteria simultaneously depend on the value of the Renyi parameter, and on the (unknown) source densities. © Springer-Verlag Berlin Heidelberg 2007.

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Vrins, F., Pham, D. T., & Verleysen, M. (2007). Is the general form of Renyi’s entropy a contrast for source separation? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4666 LNCS, pp. 129–136). Springer Verlag. https://doi.org/10.1007/978-3-540-74494-8_17

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