Active Learning in Parallel Universes

This work addresses two challenges in combination: learning with a very limited number of labeled training examples (active learning) and learning in the presence of multiple views for each object where the global model to be learned is spread out over some or all of these views (learning in parallel universes). We propose a new active learning approach which selects the best samples to query the label with the goal of improving overall model accuracy and determining which universe contributes most to the local model. The resulting combination and class-specific weighting of universes provides a significantly better classification accuracy than traditional active learning methods