论文标题
基于回归的弹性度量学习在弹性曲线的形状空间上
Regression-Based Elastic Metric Learning on Shape Spaces of Elastic Curves
论文作者
论文摘要
我们提出了一个度量学习范式，基于回归的弹性度量学习（REML），该度量范式优化了在离散曲线的歧管上的弹性度量。当所选的度量模型在离散曲线歧管上接近地理位置的数据轨迹模型时，测量回归是最准确的。当对细胞形状轨迹进行测试时，用REML学习的度量的回归具有更好的预测能力，而不是传统使用的方形速度（SRV）度量。
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.
