Self-Organizing Maps
Robust Neural Gas for the Analysis of Data with Outliers
QEST '04 Proceedings of the The Quantitative Evaluation of Systems, First International Conference
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Flexible architecture of self organizing maps for changing environments
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Growing hierarchical principal components analysis self-organizing map
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
K-dynamical self organizing maps
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
International Journal of Knowledge and Web Intelligence
| Hi-index | 0.00 |
The Growing Hierarchical Self Organizing Map (GHSOM) was introduced as a dynamical neural network model that adapts its architecture during its unsupervised training process to represents the hierarchical relation of the data. However, the dynamical algorithm of the GHSOM is sensitive to the presence of noise and outliers, and the model will no longer preserve the topology of the data space as we will show in this paper. The outliers introduce an influence to the GHSOM model during the training process by locating prototypes far from the majority of data and generating maps for few samples data. Therefore, the network will not effectively represent the topological structure of the data under study. In this paper, we propose a variant to the GHSOM algorithm that is robust under the presence of outliers in the data by being resistant to these deviations. We call this algorithm Robust GHSOM (RGHSOM). We will illustrate our technique on synthetic and real data sets.