Neural maps and topographic vector quantization
Neural Networks
Self-Organizing Maps
Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Self-organizing maps with recursive neighborhood adaptation
Neural Networks - New developments in self-organizing maps
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Self-Organizing Maps with Asymmetric Neighborhood Function
Neural Computation
Analytic Comparison of Self-Organising Maps
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Self-Organizing Maps with Non-cooperative Strategies (SOM-NC)
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Top-Down Control of Learning in Biological Self-Organizing Maps
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Visual object tracking by an evolutionary self-organizing neural network
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
A parameter in the learning rule of SOM that incorporates activation frequency
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
The parameterless self-organizing map algorithm
IEEE Transactions on Neural Networks
Quantifying the neighborhood preservation of self-organizing feature maps
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Decreasing neighborhood with distance has been identified as one of a few conditions to achieve final states in the self-organizing map (SOM) that resemble the distribution of high-dimensional input data. In the classic SOM model, best matching units (BMU) decrease their influence area as a function of distance. We introduce a modification to the SOM algorithm in which neighborhood is contemplated from the point of view of affected units, not from the view of BMUs. In our proposal, neighborhood for BMUs is not reduced, instead the rest of the units exclude some BMUs from affecting them. Each neuron identifies, from the set of BMUs that influenced it in previous epochs, those to whom it becomes refractory to for the rest of the process. Despite that the condition of decreasing neighborhood over distance is not maintained, self-organization still persists, as shown by several experiments. The maps achieved by the proposed modification have, in many cases, a lower error measure than the maps formed by SOM. Also, the model is able to remove discontinuities (kinks) from the map in a very small number of epochs, which contrasts with the original SOM model.