Abstract
In this paper we present new developments of a previous work dealing with the problem of strongly-constrained orthonormal analysis of random signals. In the former work a neural learning rule arising from the study of the dynamics of a massive system in an abstract space was introduced, and the set of equations describing the motion of such a system was directly interpreted as a learning rule for neural layers. This adaptation rule can be used to solve several problems where orthonormal matrices are involved. Here we show two applications of such an approach: one dealing with PCA and one dealing with ICA.
This research was supported by the Italian MURST. Please send comments and suggestions to the first author.
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© 1997 Springer-Verlag Berlin Heidelberg
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Fiori, S., Uncini, A., Piazza, F. (1997). Application of the MEC network to principal component analysis and source separation. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020215
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DOI: https://doi.org/10.1007/BFb0020215
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