Abstract
We propose a new generalized ICA framework in the form of a multi-layer perceptron as a density estimator. We adopt an optimization strategy based on two criteria: a minimum reconstruction error and a minimum distance from a uniform distribution. Some simulation results are also reported to validate the proposed algorithm.
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© 2002 Springer-Verlag Berlin Heidelberg
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Palmieri, F., Budillon, A. (2002). Generalized Independent Component Analysis as Density Estimation. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2002. Lecture Notes in Computer Science, vol 2486. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45808-5_8
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DOI: https://doi.org/10.1007/3-540-45808-5_8
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