Large scale metric learning from equivalence constraints

  title={Large scale metric learning from equivalence constraints},
  author={Martin K{\"o}stinger and Martin Hirzer and Paul Wohlhart and Peter M. Roth and Horst Bischof},
  journal={2012 IEEE Conference on Computer Vision and Pattern Recognition},
In this paper, we raise important issues on scalability and the required degree of supervision of existing Mahalanobis metric learning methods. Often rather tedious optimization procedures are applied that become computationally intractable on a large scale. Further, if one considers the constantly growing amount of data it is often infeasible to specify fully supervised labels for all data points. Instead, it is easier to specify labels in form of equivalence constraints. We introduce a simple… CONTINUE READING
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