Papers › Fast Exact Retrieval for Nearest-neighbor Lookup (FERN)
Fast Exact Retrieval for Nearest-neighbor Lookup (FERN)
Richard Zhu
Exact nearest neighbor search is a computationally intensive process, and even its simpler sibling -- vector retrieval -- can be computationally complex. This is exacerbated when retrieving vectors which have high-dimension d relative to the number of vectors, N, in the database. Exact nearest neighbor retrieval has been generally acknowledged to be a O(Nd) problem with no sub-linear solutions. Attention has instead shifted towards Approximate Nearest-Neighbor (ANN) retrieval techniques, many of which have sub-linear or even logarithmic time complexities. However, if our intuition from binary search problems (e.g. d=1 vector retrieval) carries, there ought to be a way to retrieve an organized representation of vectors without brute-forcing our way to a solution. For low dimension (e.g. d=2 or d=3 cases), \texttt{kd-trees} provide a O(dlogN) algorithm for retrieval. Unfortunately the algorithm deteriorates rapidly to a O(dN) solution at high dimensions (e.g. k=128), in practice. We propose a novel algorithm for logarithmic Fast Exact Retrieval for Nearest-neighbor lookup (FERN), inspired by \texttt{kd-trees}. The algorithm achieves O(dlogN) look-up with 100\% recall on 10 million d=128 uniformly randomly generated vectors.\footnote{Code available at https://github.com/RichardZhu123/ferns}
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