  Distance metric learning aims to learn from the given training data a valid
distance metric, with which the similarity between data samples can be more
effectively evaluated for classification. Metric learning is often formulated
as a convex or nonconvex optimization problem, while many existing metric
learning algorithms become inefficient for large scale problems. In this paper,
we formulate metric learning as a kernel classification problem, and solve it
by iterated training of support vector machines (SVM). The new formulation is
easy to implement, efficient in training, and tractable for large-scale
problems. Two novel metric learning models, namely Positive-semidefinite
Constrained Metric Learning (PCML) and Nonnegative-coefficient Constrained
Metric Learning (NCML), are developed. Both PCML and NCML can guarantee the
global optimality of their solutions. Experimental results on UCI dataset
classification, handwritten digit recognition, face verification and person
re-identification demonstrate that the proposed metric learning methods achieve
higher classification accuracy than state-of-the-art methods and they are
significantly more efficient in training.
