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Uncertainty sampling based active learning with diversity constraint by sparse selection

Uncertainty based active learning has been well studied for selecting informative samples to improve the performance of the classifier. One of the simplest strategy is that we always select samples with top largest uncertainties for a query. However, the selected samples may be very similar to each other, which results in little information added to update the classifier. In other words, we should avoid selecting similar samples for training the classifier. This paper addresses this problem by proposing a novel method using uncertainty based active learning algorithm with diversity constraint by sparse selection. First, uncertainty scores of unlabeled samples are obtained based on the previously trained support vector machine (SVM) classifiers. Then the sample selection is represented as a sparse modeling problem and optimal samples up to the pre-defined batch size are selected for a query. Besides that, two approximated approaches are proposed to solve the sparse problem via greedy search and quadratic programming (QP), respectively. After selection, the SVM classifiers are re-trained with new labeled data and the performance is tested on the testing dataset. We conduct several experiments on three image datasets for image classification task. The experimental results show the proposed method outperforms other four different methods and achieves promising performance.