论文标题
最近的邻居搜索双曲线嵌入
Nearest Neighbor Search for Hyperbolic Embeddings
论文作者
论文摘要
嵌入双曲线空间正在成为一种表现出层次结构的数据集的有效表示技术。这一发展激发了对能够有效从嵌入负弯曲空间中的数据点中提取知识和见解的算法的需求。我们专注于最近的邻居搜索问题，这是数据分析中的基本问题。我们提出了有效的算法解决方案，这些解决方案建立在欧几里得空间中最近邻居搜索的既定方法上，从而可以轻松地采用并与现有系统集成。我们证明了我们的技术的理论保证，并且我们的实验证明了我们方法对竞争算法对实际数据集的有效性。
Embedding into hyperbolic space is emerging as an effective representation technique for datasets that exhibit hierarchical structure. This development motivates the need for algorithms that are able to effectively extract knowledge and insights from datapoints embedded in negatively curved spaces. We focus on the problem of nearest neighbor search, a fundamental problem in data analysis. We present efficient algorithmic solutions that build upon established methods for nearest neighbor search in Euclidean space, allowing for easy adoption and integration with existing systems. We prove theoretical guarantees for our techniques and our experiments demonstrate the effectiveness of our approach on real datasets over competing algorithms.
