Corpus ID: 235829643

Optimality of the Johnson-Lindenstrauss Dimensionality Reduction for Practical Measures

@article{Bartal2021OptimalityOT,
title={Optimality of the Johnson-Lindenstrauss Dimensionality Reduction for Practical Measures},
author={Yair Bartal and Ora Nova Fandina and Kasper Green Larsen},
journal={ArXiv},
year={2021},
volume={abs/2107.06626}
}
• Published 14 July 2021
• Computer Science
• ArXiv
It is well known that the Johnson-Lindenstrauss dimensionality reduction method is optimal for worst case distortion. While in practice many other methods and heuristics are used, not much is known in terms of bounds on their performance. The question of whether the JL method is optimal for practical measures of distortion was recently raised in [BFN19] (NeurIPS’19). They provided upper bounds on its quality for a wide range of practical measures and showed that indeed these are best possible… Expand

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