论文标题
泊松学习：基于图形的半监督学习，以非常低的标签率
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
论文作者
论文摘要
我们为基于图形的半监督学习率提出了一个名为Poisson Learning的新框架，以非常低的标签率。泊松学习是出于需要解决该制度中拉普拉斯半监督学习的堕落的动机。该方法用源和下沉的放置在训练点上取代了标签值的分配，并求解了图上产生的泊松方程。事实证明，结果比拉普拉斯学习的结果更稳定和信息。 Poisson学习是有效且易于实施的，我们提出了数值实验，表明该方法优于其他最新方法，以低标签速率在MNIST，FashionMnist和Cifar-10上进行半监督学习。我们还建议对Poisson Learning（称为Poisson MBO）进行图形剪切增强，该学习具有更高的准确性，并且可以融合相对阶层大小的先验知识。
We propose a new framework, called Poisson learning, for graph based semi-supervised learning at very low label rates. Poisson learning is motivated by the need to address the degeneracy of Laplacian semi-supervised learning in this regime. The method replaces the assignment of label values at training points with the placement of sources and sinks, and solves the resulting Poisson equation on the graph. The outcomes are provably more stable and informative than those of Laplacian learning. Poisson learning is efficient and simple to implement, and we present numerical experiments showing the method is superior to other recent approaches to semi-supervised learning at low label rates on MNIST, FashionMNIST, and Cifar-10. We also propose a graph-cut enhancement of Poisson learning, called Poisson MBO, that gives higher accuracy and can incorporate prior knowledge of relative class sizes.
