Self-organizing maps
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Relevance and kernel self-organising maps
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Cluster-based under-sampling approaches for imbalanced data distributions
Expert Systems with Applications: An International Journal
Indoor Localization Based on Neural Networks for Non-dedicated ZigBee Networks in AAL
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Cluster-Based sampling approaches to imbalanced data distributions
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
| Hi-index | 0.00 |
We review a technique for creating Self-organising Maps (SOMs) in a Feature space which is nonlinearly related to the original data space. We show that convergence is remarkably fast for this method. The resulting map has two properties which are interesting from a biological perspective: first, the learning forms topology preserving mappings extremely quickly; second, the learning is most refined for those parts of the feature space which is learned first and which have most data. By considering the linear feature space, we show that it is the interaction between the overcomplete basis in which learning takes place and the mixture of one-shot and incremental learning which comprises the method that gives the method its power. Finally, as an engineering application, we show that maps representing time series data are able to successfully extract the time-dependent structure in the series.