Abstract
A new unsupervised competitive learning rule is introduced, called the Self-organizing free-topology map (Softmap) algorithm, for nonparametric density estimation. The receptive fields of the formal neurons are overlapping, radially-symmetric kernels, the radii of which are adapted to the local input density together with the weight vectors which define the kernel centers. A fuzzy code membership function is introduced in order to encompass, in a novel way, the presence of overlapping receptive fields in the competitive learning scheme. Furthermore, a computationally simple heuristic is introduced for determining the overall degree of smoothness of the resulting density estimate. Finally, the density estimation performance is compared to that of the variable kernel method, VBAR and Kohonen's SOM algorithm.
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Raekelboom, S., Van Hulle, M.M. The Softmap Algorithm. Neural Processing Letters 8, 181–192 (1998). https://doi.org/10.1023/A:1009652814885
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DOI: https://doi.org/10.1023/A:1009652814885