Active Learning for Large Multi-class Problems

Scarcity and infeasibility of human supervision for large scale multi-class classification problems necessitates active learning. Unfortunately, existing active learning methods for multi-class problems are inherently binary methods and do not scale up to a large number of classes. In this paper, we introduce a probabilistic variant of the K-Nearest Neighbor method for classification that can be seamlessly used for active learning in multi-class scenarios. Given some labeled training data, our method learns an accurate metric/kernel function over the input space that can be used for classification and similarity search. Unlike existing metric/kernel learning methods, our scheme is highly scalable for classification problems and provides a natural notion of uncertainty over class labels. Further, we use this measure of uncertainty to actively sample training examples that maximize discriminating capabilities of the model. Experiments on benchmark datasets show that the ...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Prateek Jain (University of Texas at Austin), Ashish Kapoor (Microsoft Research)
• Keywords: Active Learning, Computer Vision, CVPR 2009, Large Multi-class Problems