Abstract
The Joint Approaximative Diagonalization of Eigenmatrices (JADE)-algorithm [6] is an algebraic approach for Indenpendent Component Analysis (ICA), a recent data analysis technique. The basic assumption of ICA is a linear superposition model where unknown source signals are mixed together by a mixing matrix. The aims is to recover the sources respectively the mixing matrix based upon the mixtures with only minimum or no knowledge about the sources. We will present a neural extension of the JADE-algorithm, discuss the properties of this new extension and apply it to an arbitrary mixture of real-world images.
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Ziegaus, C., Lang, E.W. (1999). Neural implementation of the JADE-algorithm. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100516
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DOI: https://doi.org/10.1007/BFb0100516
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