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Kernel-based topographic map formation achieved with an information-theoretic approach
2002
Neural Networks
A new information-theoretic learning algorithm is introduced for kernel-based topographic map formation. The kernels are allowed to overlap and move freely in the input space, and to have differing kernel ranges. We start with Linsker's infomax principle and observe that it cannot be readily extended to our case, exactly due to the presence of kernels. We then consider Bell and Sejnowski's generalization of Linsker's infomax principle, which suggests differential entropy maximization, and add a
doi:10.1016/s0893-6080(02)00077-1
pmid:12416692
fatcat:ty2bqw6qerb33kiflrgwop5rm4