Visualizing non-metric similarities in multiple maps

  title={Visualizing non-metric similarities in multiple maps},
  author={Laurens van der Maaten and Geoffrey E. Hinton},
  journal={Machine Learning},
Techniques for multidimensional scaling visualize objects as points in a low-dimensional metric map. As a result, the visualizations are subject to the fundamental limitations of metric spaces. These limitations prevent multidimensional scaling from faithfully representing non-metric similarity data such as word associations or event co-occurrences. In particular, multidimensional scaling cannot faithfully represent intransitive pairwise similarities in a visualization, and it cannot faithfully… CONTINUE READING
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