  This article addresses extraction of physically meaningful information from
STEM EELS and EDX spectrum-images using methods of Multivariate Statistical
Analysis. The problem is interpreted in terms of data distribution in a
multi-dimensional factor space, which allows for a straightforward and
intuitively clear comparison of various approaches. A new computationally
efficient and robust method for finding physically meaningful endmembers in
spectrum-image datasets is presented. The method combines the geometrical
approach of Vertex Component Analysis with the statistical approach of Bayesian
inference. The algorithm is described in detail at an example of EELS
spectrum-imaging of a multi-compound CMOS transistor.
