Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart. Recent research has demonstrated that metric learning approaches can also be applied to trees, such as molecular structures, abstract syntax trees of computer programs, or syntax trees of natural language, by learning the cost function of an edit distance, i.e. the costs of replacing, deleting, or inserting nodes in a tree. However, learning such costs directly may yield an edit distance which violates metric axioms, is challenging to interpret, and may not generalize well. In this contribution, we propose a novel metric learning approach for trees which we call embedding edit distance learning (BEDL) and which learns an edit distance indirectly by embedding the tree nodes as vectors, such that the Euclidean distance between those vectors supports class discrimination. We learn such embeddings by reducing the distance to prototypical trees from the same class and increasing the distance to prototypical trees from different classes. In our experiments, we show that BEDL improves upon the state-of-the-art in metric learning for trees on six benchmark data sets, ranging from computer science over biomedical data to a natural-language processing data set containing over 300,000 nodes.
Soft edit distance for differentiable comparison of symbolic sequences
Tula State University（图拉国立大学）；Pirogov Russian National Research Medical University（皮罗戈夫俄罗斯国立研究医科大学）；Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences（俄罗斯科学院谢米亚金—奥夫钦尼科夫生物有机化学研究所）；National Research University Higher School of Economics（国立研究型高等经济大学）；Keldysh Institute of Applied Mathematics（克尔德什应用数学研究所）
Designing ReLU Generative Networks to Enumerate Trees with a Given Tree Edit Distance
Bioinformatics Center, Institute for Chemical Research, Department of Intelligence Sciences and Technology, Kyoto University（生物信息学中心、化学研究所、智能科学与技术系、京都大学）
On Hamming-Lipschitz Type Stability of the Subdominant (Minmax) Ultrametric: Theory and Simple Proofs
Robert Bosch Center for Cyber Physical Systems, IISc Bengaluru（印度科学学院班加罗尔分校罗伯特·博世网络物理系统中心）；Centre for Infrastructure, Sustainable Transportation and Urban Planning, IISc Bengaluru（印度科学学院班加罗尔分校基础设施、可持续交通与城市规划中心）；Department of Computer Science and Automation, IISc Bengaluru（印度科学学院班加罗尔分校计算机科学与自动化系）
