Robust measures are introduced for methods to determine statistically uncorrelated or also statistically independent components spanning data measured in a way that does not permit direct separation of these underlying components. Because of the nonlinear nature of the proposed methods, iterative methods are presented for the optimization of merit functions, and local convergence of these methods is proved. Illustrative examples are presented to demonstrate the benefits of the robust approaches, including an application to the processing of dynamic medical imaging.

中文翻译：

---

健壮的[公式：参见文本]计算几何中值以及主成分和独立成分的方法。

针对用于确定统计上不相关或统计上独立的组成部分的方法引入了鲁棒的测量方法，这些组成部分跨越了以不允许直接分离这些基础组成部分的方式测量的数据。由于所提出方法的非线性性质，提出了迭代方法来优化择优函数，并证明了这些方法的局部收敛性。给出了说明性示例，以演示鲁棒方法的好处，包括在动态医学成像处理中的应用。