uncertainty scores. Thus to enjoy the benefits of both IPM and uncertainty-based selection, we can use a compound selection criterion. For the extremely small datasets, samples should be selected only using IPM. However, as we collect more data, the uncertainty score should be integrated into the decision making process. Our proposed selection algorithm, unlike other methods, easily lends itself to such modification. At each selection iteration, instead of selecting the most correlated data with $\boldsymbol{v}$ (line 3 in Algorithm \[alg:IPM\]), we can select the samples based on the following criterion: $$m^*=\underset{m}{\arg \max} ~ \alpha \; |\boldsymbol{v}^T \Tilde{\boldsymbol{a}}_m|  + (1 - \alpha) \; q(\boldsymbol{a}_m),$$ where $q(.)$ is an uncertainty measure, e.g. variation ratios. Parameter $\alpha$ determines the relative importance of the IPM metric versus the uncertainty metric. To gradually increase the impact of $q(.)$, as the model becomes more reliable, we start by setting $\alpha = 1$ and multiply it by decay rate of $0.95$ at each active learning cycle. As it is evident in Table \[tab:AL\_results\], this compound selection criteria leads to better results for larger dataset sizes.

Learning Using Representatives {#subsec:reps}
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In this experiment, we consider the problem of learning using representatives. We find the best representatives for 