Incorporating Distribution Matching into Uncertainty for Multiple Kernel Active Learning

Due to the lack of the labeled data and the complex structures of various data, it is very hard to learn the uncertainty and representativeness accurately in active learning. In this paper, we propose a multiple kernel active learning framework that incorporates a group regularizer of distribution information into the estimation of uncertainty. The proposed method takes the advantage of multiple kernel learning to learn the kernel space in which the complex structures can be well captured by kernel weights. Meanwhile, we have developed an efficient optimization algorithm to solve the proposed method. Experimental results on twelve UCI benchmark data sets and eight subsets of ImageNet show that the proposed method outperforms several state-of-the-art active learning methods. Moreover, we also have applied the proposed method to multiple feature scenario on Caltech101, and the promising results are also obtained compared with single feature scenario.

Automated summary

This paper introduces a multiple kernel active learning framework that incorporates a group regularizer of distribution information into uncertainty estimation, achieving superior performance on UCI benchmark datasets and ImageNet. Furthermore, promising results were obtained by applying the proposed method to a multiple feature scenario on Caltech101.