Corpus ID: 262323

Non-linear Metric Learning

@inproceedings{Kedem2012NonlinearML,
  title={Non-linear Metric Learning},
  author={Dor Kedem and S. Tyree and Kilian Q. Weinberger and F. Sha and G. Lanckriet},
  booktitle={NIPS},
  year={2012}
}
  • Dor Kedem, S. Tyree, +2 authors G. Lanckriet
  • Published in NIPS 2012
  • Mathematics, Computer Science
  • In this paper, we introduce two novel metric learning algorithms, Χ2-LMNN and GB-LMNN, which are explicitly designed to be non-linear and easy-to-use. The two approaches achieve this goal in fundamentally different ways: Χ2-LMNN inherits the computational benefits of a linear mapping from linear metric learning, but uses a non-linear Χ2-distance to explicitly capture similarities within histogram data sets; GB-LMNN applies gradient-boosting to learn non-linear mappings directly in function… CONTINUE READING
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