Entropy-based kernel mixture modeling for topographic map formation.

Marc M Van Hulle

doi:10.1109/tnn.2004.828763

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Entropy-based kernel mixture modeling for topographic map formation.

Marc M Van Hulle

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Abstract

A new information-theoretic learning algorithm for kernel-based topographic map formation is introduced. In the one-dimensional case, the algorithm is aimed at uniformizing the cumulative distribution of the kernel mixture densities by maximizing its differential entropy. A nonparametric differential entropy estimator is used on which normalized gradient ascent is performed. Both differentiable and nondifferentiable kernels are in principle supported, such as Gaussian and rectangular (on/off) kernels. The relation is shown with joint entropy maximization of the kernel outputs. The learning algorithm's performance is assessed and compared with the theoretically optimal performance. A fixed-point rule is derived for the case of heterogeneous kernel mixtures. Finally, an extension of the algorithm to the multidimensional case is suggested.

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Algorithms Artificial Intelligence Computer Simulation Decision Support Techniques Entropy Information Storage and Retrieval Information Theory Models, Statistical Neural Networks, Computer Pattern Recognition, Automated Probability Learning Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't Validation Study

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Marc M Van Hulle. 2004. Entropy-based kernel mixture modeling for topographic map formation.. https://doi.org/10.1109/tnn.2004.828763

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