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An alternative proof of the vulnerability of retrieval in high intrinsic dimensionality neighborhood. (arXiv:2010.00990v2 [cs.LG] UPDATED)
May 23, 2022, 1:11 a.m. | Teddy Furon
stat.ML updates on arXiv.org arxiv.org
This paper investigates the vulnerability of the nearest neighbors search,
which is a pivotal tool in data analysis and machine learning. The
vulnerability is gauged as the relative amount of perturbation that an attacker
needs to add onto a dataset point in order to modify its neighbor rank w.r.t. a
query. The statistical distribution of this quantity is derived from simple
assumptions. Experiments on six large scale datasets validate this model up to
some outliers which are explained in term …
More from arxiv.org / stat.ML updates on arXiv.org
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