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#120 - Iolo Jones: Riemannian Diffusion Geometry, Geometric Data Analysis, Markov Chains

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Dynamic Geometric Data Analysis

This chapter explores the evolution of geometric methods in data analysis, emphasizing the challenges of integrating new data into existing systems without complete recomputation. The discussion includes diffusion processes, adaptive algorithms, and their implications for machine learning, particularly in high-dimensional spaces. Additionally, the chapter draws parallels between mathematical concepts and natural phenomena, illustrating the intricate connection between geometry and data structures.

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