论文标题
基于图形的半监督学习使用空间隔离理论
Graph Based Semi-supervised Learning Using Spatial Segregation Theory
论文作者
论文摘要
在这项工作中，我们使用竞争系统的空间隔离理论来解决基于图的半监督学习。首先，我们使用相应的竞争系统的直接类似物在连接的图上定义了离散的对应物。事实证明，该模型没有我们预期的独特解决方案。然而，我们建议梯度预测和正则化方法来达到某些解决方案。然后，我们关注的是一个略有不同的模型，该模型是根据反应扩散系统的空间隔离的最新数值结果的动机。在这种情况下，我们表明该模型具有独特的解决方案，并基于它提出了一种新型的分类算法。最后，我们提出了数值实验，表明该方法具有高标签速率和低标签速率的其他半监督学习算法。
In this work we address graph based semi-supervised learning using the theory of the spatial segregation of competitive systems. First, we define a discrete counterpart over connected graphs by using direct analogue of the corresponding competitive system. This model turns out doesn't have a unique solution as we expected. Nevertheless, we suggest gradient projected and regularization methods to reach some of the solutions. Then we focus on a slightly different model motivated from the recent numerical results on the spatial segregation of reaction-diffusion systems. In this case we show that the model has a unique solution and propose a novel classification algorithm based on it. Finally, we present numerical experiments showing the method is efficient and comparable to other semi-supervised learning algorithms at high and low label rates.
