Abstract: Metric learning is an important issue in machine learning.The measuring results will significantly affect the perfor-mance of machine learning algorithms.Current researches on metric learning mainly focus on supervised learning problems.How-ever,in real world applications,there is a large amount of data that has no label or needs to pay a high price to get labels.To handle this problem,this paper proposes an active metric learning algorithm based on support vector machines(ASVM²L),which can be used for semi-supervised learning.Firstly,a small size of samples randomly selected from the unlabeled dataset are labeled by oracles,and then these samples are used to train the support vector machine metric learner(SVM²L).According to the output measuring result,the rest unlabeled samples are classified by K-NN classifiers with different values of K,and the sample with the largest voting differences is selected and submitted to the oracle to get a label.Then,the sample is added to the training set to retrain the ASVM²L model.Repeating the above steps until the termination condition is met,then the best metric matrix can be obtained from the limited labeled samples.Comparative experiments on the standard datasets verify that the proposed ASVM²L algorithm can obtain more information with the least labeled samples without affecting the classification accuracy,and therefore has better measuring performance.

Key words: Active learning, Metric Learning, Sampling strategy, Semi-supervised learning, Support vector machine metric learning