Sammon's Nonlinear Mapping Using Geodesic Distances
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Clustering with Bregman Divergences
The Journal of Machine Learning Research
A Triangulation Method for the Sequential Mapping of Points from N-Space to Two-Space
IEEE Transactions on Computers
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Learning a locality discriminating projection for classification
Knowledge-Based Systems
Feature interval learning algorithms for classification
Knowledge-Based Systems
Extending metric multidimensional scaling with Bregman divergences
Pattern Recognition
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
Incorporating visualisation quality measures to curvilinear component analysis
Information Sciences: an International Journal
Visualizing the quality of dimensionality reduction
Neurocomputing
Hi-index | 0.07 |
The Sammon mapping has been one of the most successful nonlinear metric multidimensional scaling methods since its advent in 1969, but effort has been focused on algorithm improvement rather than on the form of the stress function. This paper further investigates using left Bregman divergences to extend the Sammon mapping and by analogy develops right Bregman divergences and reveals the mechanism that improves the performance of scaling over the Sammon mapping. The influence of data space distance preprocessing on optimisation speed is noticed. Non-stress visualisation quality measures are used to compare the configuration quality of the Sammon mapping and its extensions using both Euclidean distance and graph distance on three data sets.