The Exploration Machine – A Novel Method for Data Visualization
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.
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- 8.Ultsch, A.: Maps for the visualization of high-dimensional data spaces. In: Proceedings of the Workshop on Self-Organizing Maps 2003 (WSOM 2003), Hibikino, Kitakyushu, Japan, pp. 225–230 (2003)Google Scholar
- 9.Wismüller, A.: Exploratory Morphogenesis (XOM): A Novel Computational Framework for Self-Organization. Ph.D. thesis, Technical University of Munich, Department of Electrical and Computer Engineering (2006)Google Scholar