Optimal Cluster Preserving Embedding of Nonmetric Proximity Data

@article{Roth2003OptimalCP,
  title={Optimal Cluster Preserving Embedding of Nonmetric Proximity Data},
  author={Volker Roth and J. Laub and M. Kawanabe and J. Buhmann},
  journal={IEEE Trans. Pattern Anal. Mach. Intell.},
  year={2003},
  volume={25},
  pages={1540-1551}
}
  • Volker Roth, J. Laub, +1 author J. Buhmann
  • Published 2003
  • Mathematics, Computer Science
  • IEEE Trans. Pattern Anal. Mach. Intell.
  • For several major applications of data analysis, objects are often not represented as feature vectors in a vector space, but rather by a matrix gathering pairwise proximities. Such pairwise data often violates metricity and, therefore, cannot be naturally embedded in a vector space. Concerning the problem of unsupervised structure detection or clustering, in this paper, a new embedding method for pairwise data into Euclidean vector spaces is introduced. We show that all clustering methods… CONTINUE READING
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