Metric distances derived from cosine similarity and Pearson and Spearman correlations

Abstract

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.

5 pages, 1 figure

Cited by in corpus (4)

• Learning Audio - Sheet Music Correspondences for Score Identification and Offline Alignment
• Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks
• Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures
• "The Whole Is Greater Than the Sum of Its Parts": Optimization in Collaborative Crowdsourcing