Logdet divergence based metric learning using triplet labels

Metric learning is fundamental to lots of learning algorithms and it plays significant roles in many applications. In this paper, we present a LogDet divergence based metric learning approach to a learn Mahalanobis distance over input space of the instances. In the proposed model, the most natural constraint triplets are used as the labels of the training samples. Meanwhile, in order to avoid overfitting problem, the model uses the LogDet divergence to regularize the obtained Mahalanobis matrix as close as possible to a given matrix. Besides, a cyclic iterative algorithm is presented to solve the objective function and accelerate the metric learning process. Furthermore, this paper constructs a novel dynamic triplets building strategy to guarantee that the most useful triplets are used in every training cycle. Experiments on benchmark data sets demonstrates the proposed model achieve an improved performance when compared with the state-of-theart methods.