Self-Organizing Map with False-Neighbor Degree between Neurons for Effective Self-Organization
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A novel audio color watermarking scheme based on self-organizing map
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Adaptive FIR neural model for centroid learning in self-organizing maps
IEEE Transactions on Neural Networks
Learning topological constraints in self-organizing map
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
A sequential algorithm for training the SOM prototypes based on higher-order recursive equations
Advances in Artificial Neural Systems
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As a typical data visualization technique, self-organizing map (SOM) has been extensively applied to data clustering, image analysis, dimension reduction, and so forth. In a conventional adaptive SOM, it needs to choose an appropriate learning rate whose value is monotonically reduced over time to ensure the convergence of the map, meanwhile being kept large enough so that the map is able to gradually learn the data topology. Otherwise, the SOM's performance may seriously deteriorate. In general, it is nontrivial to choose an appropriate monotonically decreasing function for such a learning rate. In this letter, we therefore propose a novel rival-model penalized self-organizing map (RPSOM) learning algorithm that, for each input, adaptively chooses several rivals of the best-matching unit (BMU) and penalizes their associated models, i.e., those parametric real vectors with the same dimension as the input vectors, a little far away from the input. Compared to the existing methods, this RPSOM utilizes a constant learning rate to circumvent the awkward selection of a monotonically decreased function for the learning rate, but still reaches a robust result. The numerical experiments have shown the efficacy of our algorithm