A unifying objective function for topographic mappings
Neural Computation
GTM: the generative topographic mapping
Neural Computation
Self-Organizing Maps
Comparing Self-Organizing Maps
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Learning More Accurate Metrics for Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Geodesic Generative Topographic Mapping
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
On the Improvement of the Mapping Trustworthiness and Continuity of a Manifold Learning Model
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data
Similarity-Based Clustering
Analytic Comparison of Self-Organising Maps
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Scale-independent quality criteria for dimensionality reduction
Pattern Recognition Letters
Multilevel manifold learning with application to spectral clustering
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Design of a structured 3D SOM as a music archive
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
Self-Organizing Map Formation with a Selectively Refractory Neighborhood
Neural Processing Letters
Financial performance analysis of European banks using a fuzzified Self-Organizing Map
International Journal of Knowledge-based and Intelligent Engineering Systems
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Several measures have been proposed for comparing nonlinear projection methods but so far no comparisons have taken into account one of their most important properties, the trustworthiness of the resulting neighborhood or proximity relationships. One of the main uses of nonlinear mapping methods is to visualize multivariate data, and in such visualizations it is crucial that the visualized proximities can be trusted upon: If two data samples are close to each other on the display they should be close-by in the original space as well. A local measure of trustworthiness is proposed and it is shown for three data sets that neighborhood relationships visualized by the Self-Organizing Map and its variant, the Generative Topographic Mapping, are more trustworthy than visualizations produced by traditional multidimensional scalingbased nonlinear projection methods.