Unsupervised Space Partitioning for Nearest Neighbor Search. (arXiv:2206.08091v2 [cs.LG] UPDATED)

Approximate Nearest Neighbor Search (ANNS) in high dimensional spaces is
crucial for many real-life applications (e.g., e-commerce, web, multimedia,
etc.) dealing with an abundance of data. This paper proposes an end-to-end
learning framework that couples the partitioning (one critical step of ANNS)
and learning-to-search steps using a custom loss function. A key advantage of
our proposed solution is that it does not require any expensive pre-processing
of the dataset, which is one of the critical limitations of the
state-of-the-art approach. …

arxiv partitioning search space unsupervised