  We investigate two classes of transformations of cosine similarity and
Pearson and Spearman correlations into metric distances, utilising the simple
tool of metric-preserving functions. The first class puts anti-correlated
objects maximally far apart. Previously known transforms fall within this
class. The second class collates correlated and anti-correlated objects. An
example of such a transformation that yields a metric distance is the sine
function when applied to centered data.
