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During a discussion in our lab last month, a professor mentioned to me that the behavior of Self-Organizing Maps can be described in terms of repeated applications of the Moore-Penrose psuedoinverse, in a vaguely similar way to how single-layer neural networks with Hebbian learning can be described by Principle Component Analysis.

An engineering student friend of mine confirmed that the MP inverse is used for dimensionality reduction, but I can't find any material on this online. Can someone point me to an article, paper, or book on the subject?

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you might try math.stackexchange.com/questions?sort=newest –  Will Jagy Aug 22 '11 at 21:37

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