Nonlinear principal component analysis by learning nerve fields united by inhibitory connections

The authors propose a model with feedforward and a recurrent self-organizing maps model which can extract nonlinear principal components. This model consists of several learning nerve fields that are mutually inhibiting each other, and is an extension of the self-organizing overlapping maps model proposed previously by the authors. The feedforward model is sufficient for nonlinear principal component analysis, but the recurrent model with symmetrical inhibitory connections between nerve fields has additional interesting characteristics. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(3): 68–78, 2004; Published online in Wiley InterScience (). DOI 10.1002sscj.1230

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