Self-organization of shift-invariant receptive fields
Neural Networks
Self-Organizing Dynamic Graphs
Neural Processing Letters
Joint entropy maximization in kernel-based topographic maps
Neural Computation
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
3D head model retrieval in kernel feature space using HSOM
Pattern Recognition
Extracting a diagnostic gait signature
Pattern Recognition
A hybrid artificial immune system and Self Organising Map for network intrusion detection
Information Sciences: an International Journal
Shape recovery by a generalized topology preserving SOM
Neurocomputing
Self-organizing mixture models
Neurocomputing
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
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Self-organizing neural networks are usually focused on prototype learning, while the topology is held fixed during the learning process. Here we propose a method to adapt the topology of the network so that it reflects the internal structure of the input distribution. This leads to a self-organizing graph, where each unit is a mixture component of a Mixture of Gaussians (MoG). The corresponding update equations are derived from the stochastic approximation framework. Experimental results are presented to show the self-organization ability of our proposal and its performance when used with multivariate datasets.