论文标题
复合高斯分布的Riemannian几何形状：递归更改检测的应用
Riemannian geometry for Compound Gaussian distributions: application to recursive change detection
论文作者
论文摘要
提出了用于复合高斯分布的新的Riemannian几何形状。特别是，获得了Fisher信息指标，以及相应的大地测量和距离功能。这种新的几何形状应用于多变量图像时间序列的变更检测问题：开发了基于Riemannian优化的递归方法。如模拟数据所示，它允许达到最佳性能，同时在计算上更有效。
A new Riemannian geometry for the Compound Gaussian distribution is proposed. In particular, the Fisher information metric is obtained, along with corresponding geodesics and distance function. This new geometry is applied on a change detection problem on Multivariate Image Times Series: a recursive approach based on Riemannian optimization is developed. As shown on simulated data, it allows to reach optimal performance while being computationally more efficient.
