Learning the Geometry of Data: A Mathematical Review of Shape Space Analysis
A central objective of machine learning is to identify structure and patterns in data. Advances in data acquisition have increasingly produced datasets whose observations possess rich geometric form, giving rise to shape spaces that encode variability in object geometry. Such datasets arise across a wide range of disciplines, including biology, medicine, anthropology, and computer vision, where subtle geometric differences often carry important scientific information. Traditional machine learning methods, however, are frequently ill-equipped to account for the nonlinear geometric structure underlying these data. This survey synthesizes a rapidly growing body of work on shape space analysi...
| Authors | Gary P. T. Choi, Khanh Dao Duc, Shira Faigenbaum-Golovin et al. |
|---|---|
| Primary category | math.ST |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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