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Optimal principal component analysis of STEM XEDS spectrum images

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Figure

Fig. 1 Schematic showing how data matrix form a principal component. The components are sorted such as the variances of the data points in columns of D is decomposed into loading matrix P and score matrix T in PCA
Fig. 2 Objects employed for evaluation of PCA in the present paper: a shows the mean image of the experimentally characterized CMOS device and b represent a twin synthetic object generated to reproduce the key features of the real object
Table 1 Composition of  layers (phases) constituting the investigated CMOS device
Fig. 3 For synthetic datasets, it is possible to evaluate the proximity between the actually extracted principal components and the “true” ones obtained in the noise-free case
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