Robust $\ell _1$ Approaches to Computing the Geometric Median and Principal and Independent Components

Journal of Mathematical Imaging and Vision, Feb 2016

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.

This is a preview of a remote PDF: https://link.springer.com/content/pdf/10.1007%2Fs10851-016-0637-9.pdf

Stephen L. Keeling, Karl Kunisch. Robust $\ell _1$ Approaches to Computing the Geometric Median and Principal and Independent Components, Journal of Mathematical Imaging and Vision, 2016, 99-124, DOI: 10.1007/s10851-016-0637-9