Sketch metric learning
The main theme of this paper is to develop a systematic framework to learn a Mahalanobis distance metric based on matrix sketching. Within this framework, we present a novel sketch metric learning algorithm which sequentially sketches the received samples from training dataset and formulates a new kind of constraint for metric learning. This is in contrast to the traditional constraints that are only consisted of data from training dataset. In this paper, one training instance in the constraint is replaced by a pseudo center, which is generated during the sketching stage. Due to this change, our learning algorithm can focus on pushing every received sample to its corresponding similar pseudo center closer and pulling it far away from the dissimilar one. In addition, it can further achieve better performance of some kinds of time-varying process (e.g. on object tracking) than the compared related competitors. We demonstrate how to implement other methods in our algorithm framework and experiment to show that our method outperforms the competitors and relevant baselines on multiple datasets.