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.

中文翻译：

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将分布匹配纳入多核主动学习的不确定性

由于缺乏标注数据以及各种数据结构复杂，在主动学习中很难准确地学习到不确定性和代表性。在本文中，我们提出了一种多核主动学习框架，该框架将分布信息的组正则化器结合到不确定性估计中。所提出的方法利用多核学习的优势来学习核空间，其中核权重可以很好地捕获复杂结构。同时，我们开发了一种有效的优化算法来解决所提出的方法。在十二个 UCI 基准数据集和八个 ImageNet 子集上的实验结果表明，所提出的方法优于几种最先进的主动学习方法。而且，