Blind Source Separation and Equiprobabilistic Topographic Maps
Journal of VLSI Signal Processing Systems
Journal of VLSI Signal Processing Systems
Kernel-based topographic map formation by local density modeling
Neural Computation
Joint entropy maximization in kernel-based topographic maps
Neural Computation
Kernel-based topographic map formation achieved with an information-theoretic approach
Neural Networks - New developments in self-organizing maps
Vector quantization using information theoretic concepts
Natural Computing: an international journal
Distributed Range-Free Localization Algorithm Based on Self-Organizing Maps
WASA '09 Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications
Model-based clustering by probabilistic self-organizing maps
IEEE Transactions on Neural Networks
Distributed range-free localization algorithm based on self-organizing maps
EURASIP Journal on Wireless Communications and Networking - Special issue on wireless network algorithms, systems, and applications
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This paper presents an algorithm to form a topographic map resembling to the self-organizing map. The idea stems on defining an energy function which reveals the local correlation between neighboring neurons. The larger the value of the energy function, the higher the correlation of the neighborhood neurons. On this account, the proposed algorithm is defined as the gradient ascent of this energy function. Simulations on two-dimensional maps are illustrated