The different mechanisms of principal component analysis (PCA) and vector quantization are combined in an architecture of one functional layer which implements vector quantization without using winner-take-all nets. After introducing cascaded vector quantization, the paper introduces a new network (the binary cascade network) which is composed of lateral inhibited neurons for PCA. They have bell-shaped activation functions which provide binary cascaded quantization stages. It is shown that this architecture is nearly optimal in terms of resource distribution.
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