Abstract
In this paper, the linear (feed-forward) multilayer ICA algorithm is proposed for the blind separation of high-dimensional mixed signals. There are two main phases in each layer. One is the mapping phase, where a one-dimensional mapping is formed by stochastic gradient algorithm which makes the higher-correlated signals be nearer incrementally. Another is the local-ICA phase, where each neighbor pair of signals in the mapping is separated by MaxKurt algorithm. By repetition of these two phase, this algorithm can reduce an ICA criterion monotonically. Some numerical experiments show that this algorithm is quite efficient in natural image processing.
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© 2004 Springer-Verlag Berlin Heidelberg
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Matsuda, Y., Yamaguchi, K. (2004). Linear Multilayer Independent Component Analysis Using Stochastic Gradient Algorithm. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_39
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DOI: https://doi.org/10.1007/978-3-540-30110-3_39
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