Re-ranking via local embeddings: A use case with permutation-based indexing and the nSimplex projection

作者：

Highlights：

• Novel methods for refining results of permutation-based Nearest Neighbors search.

• The proposed methods exploit pivot-based data embeddings.

• The original data set is not accessed at query time as done by other refining methods.

• Extensive experimental evaluation showing great improvements in the effectiveness.

摘要

•Novel methods for refining results of permutation-based Nearest Neighbors search.•The proposed methods exploit pivot-based data embeddings.•The original data set is not accessed at query time as done by other refining methods.•Extensive experimental evaluation showing great improvements in the effectiveness.

论文关键词：Metric search,Permutation-based indexing,n-point property,nSimplex projection,Metric local embeddings,Distance bounds

论文评审过程：Received 6 August 2019, Revised 17 January 2020, Accepted 27 January 2020, Available online 13 February 2020, Version of Record 15 October 2020.

论文官网地址：https://doi.org/10.1016/j.is.2020.101506