Visualizing pairwise similarity via semidefinite programming

  title={Visualizing pairwise similarity via semidefinite programming},
  author={Amir Globerson and Sam T. Roweis},
We introduce a novel learning algorithm for binary pairwise similarity measurements on a set of objects. The algorithm delivers an embedding of the objects into a vector representation space that strictly respects the known similarities, in the sense that objects known to be similar are always closer in the embedding than those known to be dissimilar. Subject to this constraint, our method selects the mapping in which the variance of the embedded points is maximized. This has the effect of… CONTINUE READING