Functional gradient approach to probabilistic minimax active learning

Abstract

Many active learning methods select informative and representative examples by employing a parametric approach. However, there is very limited research pursuing a functional non-parametric approach to informative and representative active learning. The present study proposes a general functional approach to active learning based on a minimax objective function. Using this general algorithm, the current paper presents a specific algorithm based on a simple class of functions. Experiments show that the proposed method is efficient in selecting examples. It is interesting that the resulting algorithm can be interpreted from a spectral filtering perspective. This establishes a relationship between active learning, boosting, and spectral filtering and opens up new avenues for developing even better active learning algorithms.