  We propose a low-rank approach to learning a Mahalanobis metric from data.
Inspired by the recent geometric mean metric learning (GMML) algorithm, we
propose a low-rank variant of the algorithm. This allows to jointly learn a
low-dimensional subspace where the data reside and the Mahalanobis metric that
appropriately fits the data. Our results show that we compete effectively with
GMML at lower ranks.
