Distance metric learning (DML) 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, develop a distributed scheme learning metrics on moderate-sized subsets of data, and aggregate the results into a global solution. The technique leverages the power of parallel computation.
The algorithm of the aggregated DML (ADML) scales well with the data size and can be controlled by the partition. We theoretically analyze and provide bounds for the error induced by the distributed treatment. We have conducted experimental evaluation of the ADML, both on specially designed tests and on practical image annotation tasks. Those tests have shown that the ADML achieves the state-of-the-art performance at only a fraction of the cost incurred by most existing methods.