A New Near-linear Time Algorithm For K-nearest Neighbor Search Using A Compressed Cover Tree
Yury Elkin, Vitaliy Kurlin . Arxiv 2021 – 5 citations
Given a reference set (R) of (n) points and a query set (Q) of (m) points in a metric space, this paper studies an important problem of finding (k)-nearest neighbors of every point (q \in Q) in the set (R) in a near-linear time. In the paper at ICML 2006, Beygelzimer, Kakade, and Langford introduced a cover tree on (R) and attempted to prove that this tree can be built in (O(nlog n)) time while the nearest neighbor search can be done in (O(nlog m)) time with a hidden dimensionality factor. This paper fills a substantial gap in the past proofs of time complexity by defining a simpler compressed cover tree on the reference set (R). The first new algorithm constructs a compressed cover tree in (O(n log n)) time. The second new algorithm finds all (k)-nearest neighbors of all points from (Q) using a compressed cover tree in time (O(m(k+log n)log k)) with a hidden dimensionality factor depending on point distributions of the given sets (R,Q) but not on their sizes.