Learning in Riemannian Orbifolds

2012/04/19 by Brijnesh J. Jain, Jain, Brijnesh J., Klaus Obermayer +1 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Digital Image Processing Techniques #FOS: Computer and information sciences #Graph Theory and Algorithms #Machine Learning (cs.LG) #Topological and Geometric Data Analysis

paper · pdf · doi:10.48550/arxiv.1204.4294

openalex publication_date 2012/04/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Learning in Riemannian orbifolds is motivated by existing machine learning algorithms that directly operate on finite combinatorial structures such as point patterns, trees, and graphs. These methods, however, lack statistical justification. This contribution derives consistency results for learning problems in structured domains and thereby generalizes learning in vector spaces and manifolds.

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