This paper presents the first on-line learning method to automatically deduce the insertion, deletion and substitution edit costs of the graph edit distance. The learning method is based on embedding the substitution and deletion operations into a Euclidean space. The points in this space are classified into the ones that represent substitution edit operations and the ones that represent deletion edit operations. Thus, the learning strategy is based on deducing the hyper-plane in this space that best splits these two types of points. Any linear classifier can be used to deduce this hyper-plane, for instance LDA or SVM. The on-line method has the advantage that learning the edit costs and computing the graph edit distance with the new updated costs can be done simultaneously. Experimental validation shows that the matching accuracy is competitive with the off-line methods but without the need of the whole learning set.

本文提出了第一种在线学习方法，该方法可以自动推导图编辑距离的插入，删除和替代编辑成本。该学习方法基于将替换和删除操作嵌入到欧几里得空间中。该空间中的点分为代表替换编辑操作的点和代表删除编辑操作的点。因此，学习策略是基于推论该空间中最能将这两种类型的点分开的超平面的。任何线性分类器均可用于推导此超平面，例如LDA或SVM。在线方法的优点在于，可以同时完成学习编辑成本和计算带有新更新成本的图形编辑距离。