@inproceedings{5127,
  title = {Information-theoretic Metric Learning},
  journal = {Proceedings of the 24th Annual International Conference on Machine Learning (ICML 2007)},
  booktitle = {ICML 2007},
  abstract = {In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the distance function. We express this problem as a particular Bregman optimization problem---that of minimizing the LogDet divergence subject to linear constraints. Our resulting algorithm has several advantages over existing methods. First, our method can handle a wide variety of constraints and can optionally incorporate a prior on the distance function. Second, it is fast and scalable. Unlike most existing methods, no eigenvalue computations or semi-definite programming are required. We also present an online version and derive regret bounds for the resulting algorithm. Finally, we evaluate our method on a recent error reporting system for software called Clarify, in the context of metric learning for nearest neighbor classification, as well as on standard data sets.},
  pages = {209-216},
  editors = {Ghahramani, Z. },
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jun,
  year = {2007},
  slug = {5127},
  author = {Davis, JV. and Kulis, B. and Jain, P. and Sra, S. and Dhillon, IS.},
  month_numeric = {6}
}