Fig. 7
From: Unsupervised machine learning applied to scanning precession electron diffraction data
Unsupervised learning applied to SPED data simulated using dynamical multislice calculations a Original data with a 20 mrad precession angle. b NMF decomposition, in which the loadings have been re-scaled as in Fig. 5. The factors show pseudo-subtractive features, typical of NMF. c Cluster analysis. The high proportion of data points from the boundary means there is information shared between the cluster centres. Without precession, neither method can reproduce the original data structure