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0899-7667
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Neural Computation

March 1, 2005, Vol. 17, No. 3, Pages 503-513
(doi: 10.1162/0899766053019935)
© 2005 Massachusetts Institute of Technology
Maximum Likelihood Topographic Map Formation
Article PDF (513.41 KB)
Abstract

We introduce a new unsupervised learning algorithm for kernel-based topographic map formation of heteroscedastic gaussian mixtures that allows for a unified account of distortion error (vector quantization), log-likelihood, and Kullback-Leibler divergence.