Active Instance Sampling via Matrix Partition

Recently, batch-mode active learning has attracted a lot of attention. In this paper, we propose a novel batch-mode active learning approach that selects a batch of queries in each iteration by maximizing a natural mutual information criterion between the labeled and unlabeled instances. By employing a Gaussian process framework, this mutual information based instance selection problem can be formulated as a matrix partition problem. Although matrix partition is an NP-hard combinatorial optimization problem, we show that a good local solution can be obtained by exploiting an effective local optimization technique on a relaxed continuous optimization problem. The proposed active learning approach is independent of employed classification models. Our empirical studies show this approach can achieve comparable or superior performance to discriminative batch-mode active learning methods.