ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Improved learning of Riemannian metrics for exploratory analysis
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Clustering Using Normalized Path-Based Metric
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
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A Riemannian distance is defined which is appropriate for clustering multivariate data. This distance requires that data be first fitted with a differentiable density model allowing the definition of an appropriate Riemannian metric. A tractable approximation is developed for the case of a Gaussian mixture model and the distance is tested on artificial data, demonstrating an ability to deal with differing length scales and linearly inseparable data clusters. Further work is required to investigate performance on larger data sets.