Ensemble Methods for Boosting Visualization Models
Topology preserving mappings are great tools for data visualization and inspection in large datasets. This research presents a study of the combination of different ensemble training techniques with a novel summarization algorithm for ensembles of topology preserving models. The aim of these techniques is the increase of the truthfulness of the visualization of the dataset obtained by this kind of algorithms and, as an extension, the stability conditions of the former. A study and comparison of the performance of some novel and classical ensemble techniques, using well-known datasets from the UCI repository (Iris and Wine), are presented in this paper to test their suitability, in the fields of data visualization and topology preservation when combined with one of the most widespread of that kind of models such as the Self-Organizing Map.
Keywordstopology preserving mappings boosting bagging unsupervised learning
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