Hyperbolic Classifier

Hyperbolic classifiers leverage the unique geometric properties of hyperbolic space to improve the performance of machine learning models, particularly for data exhibiting hierarchical structures. Current research focuses on developing efficient algorithms, such as hyperbolic decision trees and random forests, and addressing numerical stability issues inherent in hyperbolic computations, often employing techniques like tangent space formalisms or specific model choices (e.g., Lorentz model). These advancements are proving valuable in diverse applications, including face anti-spoofing, object detection, and fine-grained aspect extraction, demonstrating superior performance compared to Euclidean counterparts in handling complex, hierarchical data.