Simplicial complexes form an important class of topological spaces that are frequently used to in many applications areas such as computer-aided design, computer graphics, and simulation. The representation learning on graphs, which are just 1-d simplicial complexes, has witnessed a great attention and success in the past few years. Due to the additional complexity higher dimensional simplicial hold, there has not been enough effort to extend representation learning to these objects especially when it comes to learn entire-simplicial complex representation. In this work, we propose a method for simplicial complex-level representation learning that embeds a simplicial complex to a universal embedding space in a way that complex-to-complex proximity is preserved. Our method utilizes a simplex-level embedding induced by a pre-trained simplicial autoencoder to learn an entire simplicial complex representation. To the best of our knowledge, this work presents the first method for learning simplicial complex-level representation.
翻译:简易复合体是许多应用领域(例如计算机辅助设计、计算机图形和模拟)经常使用的重要的表层空间。图形上的演示学习只是一维简易复合体,在过去几年中受到极大关注并取得了很大成功。由于更高维的简单手持的复杂程度,没有作出足够的努力,将演示学习扩大到这些物体,特别是在学习整个简易复杂代表物时。在这项工作中,我们提出了一个简化复杂层次的代表学习方法,将一个简单复杂的综合体嵌入一个普遍的嵌入空间,以保持复杂至复合的近距离。我们的方法使用一个经过事先训练的简易自动解密器的简单化嵌入层,学习整个简易复杂的代表物。在我们的知识中,这项工作是学习简易复杂层次代表物的第一种方法。