Self-organizing maps
A stochastic self-organizing map for proximity data
Neural Computation
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
Fast algorithm and implementation of dissimilarity self-organizing maps
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
| Hi-index | 0.00 |
Adaptation of the Self-Organizing Map to dissimilarity data is of a growing interest. For many applications, vector representation is not available and but only proximity data (distance, dissimilarity, similarity, ranks ...). In this article, we present a new adaptation of the SOM algorithm which is compared with two existing ones. Three metrics for quality estimate (quantization and neighborhood) are used for comparison. Numerical experiments on artificial and real data show the algorithm quality. The strong point of the proposed algorithm comes from a more accurate prototype estimate which is one of the difficult parts of Dissimilarity SOM algorithms (DSOM).