We study metric learning as a problem of information retrieval. We
present a general metric learning algorithm, based on the structural SVM
framework, to learn a metric such that rankings of data induced by dis- tance
from a query can be optimized against various ranking measures, such as AUC,
Precision-at-k, MRR, MAP or NDCG. We demonstrate ex- perimental results on
standard classication data sets, and a large-scale online dating
recommendation problem.
Pre-2018 CSE ID: CS2010-0955
