Optimal principal component analysis of STEM XEDS spectrum images
Open Access
- 9 April 2019
- journal article
- research article
- Published by Springer Science and Business Media LLC in Advanced Structural and Chemical Imaging
- Vol. 5 (1), 1-21
- https://doi.org/10.1186/s40679-019-0066-0
Abstract
STEM XEDS spectrum images can be drastically denoised by application of the principal component analysis (PCA). This paper looks inside the PCA workflow step by step on an example of a complex semiconductor structure consisting of a number of different phases. Typical problems distorting the principal components decomposition are highlighted and solutions for the successful PCA are described. Particular attention is paid to the optimal truncation of principal components in the course of reconstructing denoised data. A novel accurate and robust method, which overperforms the existing truncation methods is suggested for the first time and described in details.Funding Information
- European Research Council (715620)
- Deutsche Forschungsgemeinschaft (F-003661-553-Ü6a-1020605)
- Open Access Publishing Funds of the SLUB / TU Dresden
This publication has 32 references indexed in Scilit:
- Enhanced Detection Sensitivity with a New Windowless XEDS System for AEM Based on Silicon Drift Detector TechnologyMicroscopy Today, 2010
- Two-stage image denoising by principal component analysis with local pixel groupingPattern Recognition, 2010
- Finite sample approximation results for principal component analysis: A matrix perturbation approachThe Annals of Statistics, 2008
- Tomographic Spectral Imaging with Multivariate Statistical Analysis: Comprehensive 3D MicroanalysisMicroscopy and Microanalysis, 2006
- Accounting for Poisson noise in the multivariate analysis of ToF‐SIMS spectrum imagesSurface and Interface Analysis, 2004
- MULTIVARIATE STATISTICAL ANALYSIS OF FEG‐STEM EDX SPECTRAJournal of Microscopy, 1996
- Factor Analysis in ChemistryTechnometrics, 1994
- Theory of error in factor analysisAnalytical Chemistry, 1977
- Measures of multivariate skewness and kurtosis with applicationsBiometrika, 1970
- Analysis of a complex of statistical variables into principal components.Journal of Educational Psychology, 1933