论文标题
从数据到使用图形卷积网络的帮助分类任务的几何图
Geometric graphs from data to aid classification tasks with graph convolutional networks
论文作者
论文摘要
传统的分类任务学会仅根据样本特征将样本分配给给定的类。这种范式正在发展为包括其他信息来源，例如样本之间的已知关系。在这里，我们表明，即使数据集中没有其他关系信息，也可以通过从功能本身构造几何图并在图形卷积网络中使用它们来改善分类。分类准确性的提高是通过捕获相对较低边缘密度的样品相似性的图表最大化的。我们表明，这种特征衍生的图会增加数据与地面真相的一致性，同时改善了班级分离。我们还证明，使用光谱稀疏性可以使图表更有效，从而减少了边缘的数量，同时仍改善了分类性能。我们使用来自各种科学领域的合成和现实世界数据集说明了我们的发现。
Traditional classification tasks learn to assign samples to given classes based solely on sample features. This paradigm is evolving to include other sources of information, such as known relations between samples. Here we show that, even if additional relational information is not available in the data set, one can improve classification by constructing geometric graphs from the features themselves, and using them within a Graph Convolutional Network. The improvement in classification accuracy is maximized by graphs that capture sample similarity with relatively low edge density. We show that such feature-derived graphs increase the alignment of the data to the ground truth while improving class separation. We also demonstrate that the graphs can be made more efficient using spectral sparsification, which reduces the number of edges while still improving classification performance. We illustrate our findings using synthetic and real-world data sets from various scientific domains.
