Neighbor Embedding XOM for Dimension Reduction and Visualization

2011

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

Link

@article{BunteNEXOM2010,
author = {Kerstin Bunte and Barbara Hammer and Thomas Villmann and Michael Biehl and Axel Wism\"uller},
title = {{Neighbor Embedding XOM for Dimension Reduction and Visualization}},
journal = {Neurocomputing},
volume = {74},
number = {9},
pages = {1340--1350},
year = {2011},
issn = {0925-2312},
doi = {10.1016/j.neucom.2010.11.027},
url = {http://dx.doi.org/10.1016/j.neucom.2010.11.027},
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},
}