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Linear Time Heuristics for Topographic Mapping of Dissimilarity Data

  • Andrej Gisbrecht
  • Frank-Michael Schleif
  • Xibin Zhu
  • Barbara Hammer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6936)

Abstract

Topographic mapping offers an intuitive interface to inspect large quantities of electronic data. Recently, it has been extended to data described by general dissimilarities rather than Euclidean vectors. Unlike its Euclidean counterpart, the technique has quadratic time complexity due to the underlying quadratic dissimilarity matrix. Thus, it is infeasible already for medium sized data sets. We introduce two approximation techniques which speed up the complexity to linear time algorithms: the Nyström approximation and patch processing, respectively. We evaluate the techniques on three examples from the biomedical domain.

Keywords

Topographic Mapping Dissimilarity Matrix Biomedical Domain Generalize Linear Regression Model Generative Topographic Mapping 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andrej Gisbrecht
    • 1
  • Frank-Michael Schleif
    • 1
  • Xibin Zhu
    • 1
  • Barbara Hammer
    • 1
  1. 1.CITEC Centre of ExcellenceBielefeld UniversityBielefeldGermany

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