In an era driven by complex data, scientists are increasingly encountering information that doesn’t lie neatly on flat, Euclidean surfaces. From 3D medical scans to robot orientations and AI transformations, much of today’s data lives on curved geometric spaces, called Riemannian manifolds. Analyzing such data accurately has remained a challenge, especially when noise or outliers distort results.
Robust ‘Huber mean’ for geometric data protects against noise and outliers
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