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

We propose a new metric learning paradigm, Regression-based Elastic Metric Learning (REML), which optimizes the elastic metric for manifold regression on the manifold of discrete curves. Our method recognizes that the "ideal" metric is trajectory-dependent and thus creates an opportunity for improved regression fit on trajectories of curves. When tested on cell shape trajectories, REML's learned metric generates a better regression fit than the conventionally used square-root-velocity SRV metric.