Joint entropy maximization in kernel-based topographic maps.

@article{Hulle2002JointEM,
  title={Joint entropy maximization in kernel-based topographic maps.},
  author={Marc M. Van Hulle},
  journal={Neural computation},
  year={2002},
  volume={14 8},
  pages={1887-906}
}
A new learning algorithm for kernel-based topographic map formation is introduced. The kernel parameters are adjusted individually so as to maximize the joint entropy of the kernel outputs. This is done by maximizing the differential entropies of the individual kernel outputs, given that the map's output redundancy, due to the kernel overlap, needs to be minimized. The latter is achieved by minimizing the mutual information between the kernel outputs. As a kernel, the (radial) incomplete gamma… CONTINUE READING

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