论文标题
表示关系数据的嵌套子空间安排
Nested Subspace Arrangement for Representation of Relational Data
论文作者
论文摘要
许多研究人员在机器学习领域进行了有关获得离散对象的适当连续表示的研究，例如图形和知识库数据。在这项研究中，我们介绍了嵌套子空间（NSS）布置，这是代表学习的综合框架。我们表明，现有的嵌入技术可以被视为NSS布置的特殊情况。基于NSS安排的概念，我们实施了磁盘锚定安排（Dancar），这是一种专门用于复制一般图形的表示方法。数值实验表明，Dancar在重建任务中成功将Wordnet嵌入了$ {\ Mathbb r}^{20} $中，F1得分为0.993。 Dancar也适合可视化理解图形的特征。
Studies on acquiring appropriate continuous representations of discrete objects, such as graphs and knowledge base data, have been conducted by many researchers in the field of machine learning. In this study, we introduce Nested SubSpace (NSS) arrangement, a comprehensive framework for representation learning. We show that existing embedding techniques can be regarded as special cases of the NSS arrangement. Based on the concept of the NSS arrangement, we implement a Disk-ANChor ARrangement (DANCAR), a representation learning method specialized to reproducing general graphs. Numerical experiments have shown that DANCAR has successfully embedded WordNet in ${\mathbb R}^{20}$ with an F1 score of 0.993 in the reconstruction task. DANCAR is also suitable for visualization in understanding the characteristics of graphs.
