The Exploration Machine --- A Novel Method for Data Visualization

Authors:
Axel Wismüller
Affiliations:
Depts. of Radiology and Biomedical Engineering, University of Rochester, New York, U.S.A. NY 14642-8648
Venue:
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Year:
2009

Citing 3
Cited 3

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Abstract

We present a novel method for structure-preserving dimensionality reduction. The Exploration Machine (Exploratory Observation Machine, XOM) computes graphical representations of high-dimensional observations by a strategy of self-organized model adaptation. Although simple and computationally efficient, XOM enjoys a surprising flexibility to simultaneously contribute to several different domains of advanced machine learning, scientific data analysis, and visualization, such as structure-preserving dimensionality reduction and data clustering.