PCA is only one item from a large collection of techniques that are aimed at keeping the data manageable and finding patterns in it, a collection that also includes kernel methods and support vector machines (e.g., Vapnik and Kotz 2006). For present purposes, it is important to stress that such tools should not be confused with statistical analysis: they do not involve the testing or evaluation of distributions over sample space, even though they build up and evaluate models of the data. This sets them apart from, e.g., confirmatory and exploratory factor analysis (Bartholomew 2008), which is sometimes taken to be a close relative of PCA because both sets of techniques allows us to identify salient dimensions within sample space, along which the data show large variation. 