K-edge connected neighborhood graph for geodesic distance estimation and nonlinear data projection

@article{Yang2004KedgeCN,
  title={K-edge connected neighborhood graph for geodesic distance estimation and nonlinear data projection},
  author={Lei Yang},
  journal={Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.},
  year={2004},
  volume={1},
  pages={196-199 Vol.1}
}
Nonlinear data projection based on geodesic distances requires the construction of a neighborhood graph that spans all data points so that the geodesic distance between any pair of data points could be estimated by the graph distance between the pair. This paper proposes an approach for constructing a k-edge connected neighborhood graph. The approach works by repeatedly extracting minimum spanning trees from the complete Euclidean graph of all data points. The constructed neighborhood graph has… CONTINUE READING
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