Neighbor embedding XOM for dimension reduction and visualization

Authors:
Kerstin Bunte;Barbara Hammer;Thomas Villmann;Michael Biehl;Axel Wismüller
Affiliations:
Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700AK Groningen, The Netherlands and Department of Radiology, University of Rochester, 601 Elmwood Avenue ...;Bielefeld University, CITEC, Universitätsstraíe 23, 33615 Bielefeld, Germany;Department of Mathematics, University of Applied Sciences Mittweida, Germany;Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700AK Groningen, The Netherlands;Department of Radiology, University of Rochester, 601 Elmwood Avenue, Rochester, NY 14642-648, USA and Department of Biomedical Engineering, University of Rochester, 601 Elmwood Avenue, Rochester, ...
Venue:
Neurocomputing
Year:
2011

Citing 16
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Abstract

We present an extension of the Exploratory Observation Machine (XOM) for structure-preserving dimensionality reduction. Based on minimizing the Kullback-Leibler divergence of neighborhood functions in data and image spaces, this Neighbor Embedding XOM (NE-XOM) creates a link between fast sequential online learning known from topology-preserving mappings and principled direct divergence optimization approaches. We quantitatively evaluate our method on real-world data using multiple embedding quality measures. In this comparison, NE-XOM performs as a competitive trade-off between high embedding quality and low computational expense, which motivates its further use in real-world settings throughout science and engineering.