  Learning Mahanalobis distance metrics in a high- dimensional feature space is
very difficult especially when structural sparsity and low rank are enforced to
improve com- putational efficiency in testing phase. This paper addresses both
aspects by an ensemble metric learning approach that consists of sparse block
diagonal metric ensembling and join- t metric learning as two consecutive
steps. The former step pursues a highly sparse block diagonal metric by
selecting effective feature groups while the latter one further exploits
correlations between selected feature groups to obtain an accurate and low rank
metric. Our algorithm considers all pairwise or triplet constraints generated
from training samples with explicit class labels, and possesses good scala-
bility with respect to increasing feature dimensionality and growing data
volumes. Its applications to face verification and retrieval outperform
existing state-of-the-art methods in accuracy while retaining high efficiency.
