Abstract
New algorithms for neural Principal Component and Independent Component Analysis (PCA and ICA) are introduced. Special emphasis is laid on the locality of learning. This enables a simpler hardware implementation, and may provide a more plausible model of biological neurons. To achieve this, the algorithms feature a new kind of feedback which is multiplicative and anti-Hebbian. The convergence of the ICA algorithm is proven analytically in the general case; the convergence of the PCA algorithm is proven for Gaussian data.
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© 1996 Springer-Verlag Berlin Heidelberg
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Hyvärinen, A. (1996). Purely local neural Principal Component and Independent Component Learning. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_27
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DOI: https://doi.org/10.1007/3-540-61510-5_27
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Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-540-68684-2
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