Nearest-neighbor-preserving embeddings

Authors:
Piotr Indyk;Assaf Naor
Affiliations:
MIT, Cambridge, MA;Microsoft Research, New York, NY
Venue:
ACM Transactions on Algorithms (TALG)
Year:
2007

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Abstract

In this article we introduce the notion of nearest-neighbor-preserving embeddings. These are randomized embeddings between two metric spaces which preserve the (approximate) nearest-neighbors. We give two examples of such embeddings for Euclidean metrics with low “intrinsic” dimension. Combining the embeddings with known data structures yields the best-known approximate nearest-neighbor data structures for such metrics.