Hyperbolic Data

Hyperbolic data analysis focuses on representing and processing data with inherent hierarchical structures using hyperbolic geometry, which offers advantages over Euclidean approaches for such data. Current research emphasizes developing efficient algorithms for tasks like dimensionality reduction (e.g., hyperbolic PCA), clustering (e.g., hyperbolic hierarchical clustering), and classification (e.g., hyperbolic decision trees and random forests), often incorporating contrastive learning and novel distance metrics. These advancements improve the accuracy and scalability of machine learning models for applications involving hierarchical data, such as bioinformatics and information retrieval, by better capturing the underlying relationships within the data.