Journal
ULTRAMICROSCOPY
Volume 160, Issue -, Pages 197-212Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ultramic.2015.10.020
Keywords
Principal Component Analysis; PCA loading; Scatterplot; Noise; Filtering; PCA accuracy; Clustering
Categories
Ask authors/readers for more resources
Principal Component Analysis (PCA) can improve dramatically the treatment of large STEM spectrum-images by finding the directions (loadings) of highest data variance in the factor space and projecting the data on these directions. Loadings typically do not show clear physical meanings, thus the interpretation of PCA results is difficult. This work investigates the potential reasons for appearing such counterintuitive PCA outputs. The following reasons are identified: (i) missing the step of centering the data in the PCA pre-treatment, (ii) complexity of data variations inconsistent with the orthogonality restrictions of PCA, (iii) non-linearity caused either by chemical variations or by the peculiarities of the spectra formation, and (iv) inaccuracy in extracting major PCA components. In many cases, the PCA treatment can be altered in such a way that the intuitively clear loadings are delivered. (C) 2015 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available