Reviews: Measures of distortion for machine learning

SUMMARY When points in one metric space are embedded into another (eg. This paper is a systematic study of distortion measures. It formally defines several desired properties of a distortion measure, and compares existing distortion measures from the lens of those formal definitions and by simulations on synthetic data. Based on these, the pros and cons of each measure are discussed, and a new notion of distortion is suggested. COMMENTS The systematic study of desirable properties of distortion is solid and in my view constitutes the main strength of this submission.