| Literature DB >> 27471346 |
Stephen L Keeling1, Karl Kunisch1.
Abstract
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.Entities:
Keywords: Geometric median; Independent component analysis; Local convergence of iterative methods; Robustness; principal component analysis
Year: 2016 PMID: 27471346 PMCID: PMC4946825 DOI: 10.1007/s10851-016-0637-9
Source DB: PubMed Journal: J Math Imaging Vis ISSN: 0924-9907 Impact factor: 1.627