A bit of a meta question, but what is the current thinking regarding the use of PCA in understanding data(sets)? My recollection is that PCA was somewhat en vogue in astronomy 8–10 years ago. I saw it applied to various mid-infrared data, but it seemed difficult to actually translate the principal components into useful physical knoweldge about the datasets or the astronomical objects. Since then, I rarely see astronomy papers with PCA analysis, and even then, the PCA analysis doesn&#x27;t seem to contribute much to the physical understanding of the objects being studied.<p>Is this just a case of PCA being ill-suited to the analysis of these datasets in astronomy? Or is it a more general problem that PCA can reduce datasets to arbitrary component vectors but those vectors may not contain easily-quanitifiable physical information (but might contain predictive power, if an understanding of the underlying physical system is not the goal)?