Authors: Zhangyu Wang, Lantian Xu, Zhifeng Kong, Weilong Wang, Xuyu Peng, Enyang Zheng
Abstract: Hyperbolic embeddings are a class of representation learning methods that
offer competitive performances when data can be abstracted as a tree-like
graph. However, in practice, learning hyperbolic embeddings of hierarchical
data is difficult due to the different geometry between hyperbolic space and
the Euclidean space. To address such difficulties, we first categorize three
kinds of illness that harm the performance of the embeddings. Then, we develop
a geometry-aware algorithm using a dilation operation and a transitive closure
regularization to tackle these illnesses. We empirically validate these
techniques and present a theoretical analysis of the mechanism behind the
dilation operation. Experiments on synthetic and real-world datasets reveal
superior performances of our algorithm.
Source: http://arxiv.org/abs/2407.16641v1
