Literature DB >> 27471346

Robust [Formula: see text] Approaches to Computing the Geometric Median and Principal and Independent Components.

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


  5 in total

1.  Minimizing macrovessel signal in cerebral perfusion imaging using independent component analysis.

Authors:  G Reishofer; F Fazekas; S Keeling; C Enzinger; F Payer; J Simbrunner; R Stollberger
Journal:  Magn Reson Med       Date:  2007-02       Impact factor: 4.668

2.  Principal component analysis based on l1-norm maximization.

Authors:  Nojun Kwak
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2008-09       Impact factor: 6.226

3.  A Pure L1-norm Principal Component Analysis.

Authors:  Jp Brooks; Jh Dulá; El Boone
Journal:  Comput Stat Data Anal       Date:  2013-05-01       Impact factor: 1.681

4.  Robust independent component analysis by iterative maximization of the kurtosis contrast with algebraic optimal step size.

Authors:  Vicente Zarzoso; Pierre Comon
Journal:  IEEE Trans Neural Netw       Date:  2009-12-18

5.  Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis.

Authors:  Gert Wollny; Peter Kellman; Andrés Santos; María J Ledesma-Carbayo
Journal:  Med Image Anal       Date:  2012-02-23       Impact factor: 8.545

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.