On the loss of information in PCA of spectrum-images

Pavel Potapov

doi:10.1016/j.ultramic.2017.06.023

On the loss of information in PCA of spectrum-images

Ultramicroscopy. 2017 Nov:182:191-194. doi: 10.1016/j.ultramic.2017.06.023. Epub 2017 Jul 6.

Author

Pavel Potapov¹

Affiliation

¹ Leibniz Institute for Solid State and Material Research, Helmholtzstraße 20, 01069 Dresden, Germany. Electronic address: pavel@temdm.com.

PMID: 28697383
DOI: 10.1016/j.ultramic.2017.06.023

Abstract

Principal Component Analysis (PCA) can drastically denoise STEM spectrum-images but might distort or cut off the important variations in data. The present paper analyzes various approaches to estimate such deviations and compares them with the simulated data. A spiked covariance model by Nadler (2008) appears to be most appropriated for application in STEM spectrum-imaging.

Publication types

Research Support, Non-U.S. Gov't