Search Machine Learning Repository: @inproceedings{icml2014c2_wangc14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Two-Stage Metric Learning},
    Url = {http://jmlr.org/proceedings/papers/v32/wangc14.pdf},
    Abstract = {In this paper, we present a novel two-stage metric learning algorithm. We first map each learning instance to a probability distribution by computing its similarities to a set of fixed anchor points. Then, we define the distance in the input data space as the Fisher information distance on the associated statistical manifold. This induces in the input data space a new family of distance metric which presents unique properties. Unlike kernelized metric learning, we do not require the similarity measure to be positive semi-definite. Moreover, it can also be interpreted as a local metric learning algorithm with well defined distance approximation. We evaluate its performance on a number of datasets. It outperforms significantly other metric learning methods and SVM.},
    Author = {Jun Wang and Ke Sun and Fei Sha and St├ęphane Marchand-maillet and Alexandros Kalousis},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {370-378}
   }