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We investigate two classes of transformations of cosine similarity andPearson and Spearman correlations into metric distances, utilising the simpletool of metric-preserving functions. The first class puts anti-correlatedobjects maximally far apart. Previously known transforms fall within thisclass. The second class collates correlated and anti-correlated objects. Anexample of such a transformation that yields a metric distance is the sinefunction when applied to centered data.

Metric distances derived from cosine similarity and Pearson and Spearman correlations