Active learning via neighborhood reconstruction

In many real world scenarios, active learning methods are used to select the most informative points for labeling to reduce the expensive human action. One direction for active learning is selecting the most representative points, ie., selecting the points that other points can be approximated by linear combination of the selected points. However, these methods fails to consider the local geometrical information of the data space. In this paper, we propose a novel framework named Active Learning via Neighborhood Reconstruction (ALNR) by taking into account the locality information directly during the selection. Specifically, for the linear reconstruction of target point, the nearer neighbors should have a greater effect and the selected points distant from the target point should be penalized severely. We further develop an efficient two-stage iterative procedure to solve the final optimization problem. Our empirical study shows encouraging results of the proposed algorithms in comparison to other state-of-the-art active learning algorithms on both synthetic and real visual data sets.