Regression-Based Elastic Metric Learning on Shape Spaces of Elastic Curves

2022/10/04 by Adele Myers, Nina Miolane, Myers, Adele +1
Computer Science · Mathematics · Pharmacology, Toxicology and Pharmaceutics · #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #Machine Learning (cs.LG) #Medicinal Plant Pharmacodynamics Research #Morphological variations and asymmetry

paper · pdf · doi:10.48550/arxiv.2210.01932

openalex publication_date 2022/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose a metric learning paradigm, Regression-based Elastic Metric Learning (REML), which optimizes the elastic metric for geodesic regression on the manifold of discrete curves. Geodesic regression is most accurate when the chosen metric models the data trajectory close to a geodesic on the discrete curve manifold. When tested on cell shape trajectories, regression with REML's learned metric has better predictive power than with the conventionally used square-root-velocity (SRV) metric.

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