| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 5466700 | Ultramicroscopy | 2017 | 4 Pages |
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.
Related Topics
Physical Sciences and Engineering
Materials Science
Nanotechnology
Authors
Pavel Potapov,