论文标题
Poisson重新加权的Laplacian不确定性抽样，以基于图的主动学习
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active Learning
论文作者
论文摘要
我们表明，只要不确定性的度量与基本模型正确对齐，并且该模型正确反映了未开发的区域中的不确定性，则不确定性抽样足以实现基于图的主动学习的探索与剥削。特别是，我们为分类器使用最近开发的算法，Poisson重新持续的拉普拉斯学习（PWLL），并引入了一种采集函数，旨在测量此基于图的分类器中的不确定性，以识别未探索数据的区域。我们在PWLL中引入了对角扰动，该扰动产生了解决方案的指数定位，并控制了积极学习中的探索与剥削权衡。我们使用PWLL的良好连续限量来严格分析我们的方法，并就许多基于图的图像分类问题提出实验结果。
We show that uncertainty sampling is sufficient to achieve exploration versus exploitation in graph-based active learning, as long as the measure of uncertainty properly aligns with the underlying model and the model properly reflects uncertainty in unexplored regions. In particular, we use a recently developed algorithm, Poisson ReWeighted Laplace Learning (PWLL) for the classifier and we introduce an acquisition function designed to measure uncertainty in this graph-based classifier that identifies unexplored regions of the data. We introduce a diagonal perturbation in PWLL which produces exponential localization of solutions, and controls the exploration versus exploitation tradeoff in active learning. We use the well-posed continuum limit of PWLL to rigorously analyze our method, and present experimental results on a number of graph-based image classification problems.
