  Distance metric learning is successful in discovering intrinsic relations in
data. However, most algorithms are computationally demanding when the problem
size becomes large. In this paper, we propose a discriminative metric learning
algorithm, and develop a distributed scheme learning metrics on moderate-sized
subsets of data, and aggregating the results into a global solution. The
technique leverages the power of parallel computation. The algorithm of the
aggregated distance metric learning (ADML) scales well with the data size and
can be controlled by the partition. We theoretically analyse and provide bounds
for the error induced by the distributed treatment. We have conducted
experimental evaluation of ADML, both on specially designed tests and on
practical image annotation tasks. Those tests have shown that ADML achieves the
state-of-the-art performance at only a fraction of the cost incurred by most
existing methods.
