Abstract
This paper presents a new approach to recover original signals (`sources') from their linear mixtures, observed by the same number of sensors. The algorithms proposed assume that the input distributions are bounded and that the sources generate certain combinations of extreme values (`critical vectors'). The idea is very simple and is based on geometric algebra properties. We present a neural network approach to show that with two networks, one for the separation of sources and one for weight learning, running in parallel, it is possible to efficiently recover the original signals. The learning rule is unsupervised and each computational element uses only local information. Preliminary results obtained from experiments with synthetic and real signals are included to show the potential and limitations of the procedure.
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