Topology representing networks
Neural Networks
Self-organizing maps
Nonlinear Projection with the Isotop Method
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Online data visualization using the neural gas network
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Nonlinear projection using geodesic distances and the neural gas network
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Interactive visualization and analysis of hierarchical neural projections for data mining
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
A robust nonlinear projection method based on self-organizing neural networks is proposed. The neural gas algorithm along with the competitive Hebbian learning rule are used to quantize the data samples and construct a neighborhood graph in input space. The resulting graph is used to estimate geodesic distances. The proposed projection method minimizes a cost function that depends on the interpoint distances, and favors local topologies. The projection is done in two steps to avoid errors due to shortcuts in the neighborhood graph when dealing with noisy and/or non-uniformly distributed data sets. The proposed nonlinear projection method outperformed alternative methods such as curvilinear distance analysis and geodesic nonlinear projection in terms of trustworthiness, continuity and topology preservation measurements, using two benchmark data sets: noisy Swiss Roll and Iris.