cost significantly, by selecting the most informative unlabeled samples. Next, in Section \[subsec:reps\], we show the effectiveness of IPM in selecting the most informative representatives, by training the classifier using only a few representatives from each class. Lastly, in Section \[subsec:exp\_vid\_sum\], the application of IPM for video summarization is exhibited. In addition, we investigate the robustness and other performance metrics, such as projection error and running time, of different selection methods and verify our theoretical results in the supplementary material.

Active Learning {#subsec:active-learning}
---------------

*Active learning* aims at addressing the costly data labeling problem by iteratively training a model using a small number of labeled data, and then querying the labels of some selected data, using an acquisition function.

In active learning, the model is initially trained using a small set of labeled data (the initial training set). Then, the acquisition function selects a few points from the pool of unlabeled data, asks an oracle (often a human expert) for the labels, and adds them to the training set. Next, a new model is trained on the updated training set. By repeating these steps, we can collect the *most informative* samples, which often result in significant reductions in the labeling 