Horospherical Decision Boundaries for Large Margin Classification in Hyperbolic Space

Xiran Fan, Chun-Hao Yang, Baba C. Vemuri

Keywords: Large-margin clssifier, Hyperbolic space, Horosphere, SVM, Geodesically convex, Global optimility, Busemann function

TL;DR: Geodesically convex Large-margin classifier in hyperbolic space

Abstract: Hyperbolic spaces have been quite popular in the recent past for representing hierarchically organized data. Further, several classification algorithms for data in these spaces have been proposed in the literature. These algorithms mainly use either hyperplanes or geodesics for decision boundaries in a large margin classifiers setting leading to a non-convex optimization problem. In this paper, we propose a novel large margin classifier based on horospherical decision boundaries that leads to a geodesically convex optimization problem that can be optimized using any Riemannian gradient descent technique guaranteeing a globally optimal solution. We present several experiments depicting the competitive performance of our classifier in comparison to SOTA.

Submission Number: 5097