Fast spectral analysis for approximate nearest neighbor search

Fast Approximate Nearest-Neighbor Search with k-Nearest Neighbor Graph

We introduce a new nearest neighbor search algorithm. The algorithm builds a nearest neighbor graph in an offline phase and when queried with a new point, performs hill-climbing starting from a randomly sampled node of the graph. We provide theoretical guarantees for the accuracy and the computational complexity and empirically show the effectiveness of this algorithm.

Composite Quantization for Approximate Nearest Neighbor Search

This paper presents a novel compact coding approach, composite quantization, for approximate nearest neighbor search. The idea is to use the composition of several elements selected from the dictionaries to accurately approximate a vector and to represent the vector by a short code composed of the indices of the selected elements. To efficiently compute the approximate distance of a query to a ...

Approximate Nearest Neighbor Search in $\ell_p$

We present a new locality sensitive hashing (LSH) algorithm for c-approximate nearest neighbor search in lp with 1 < p < 2. For a database of n points in lp, we achieve O(dn ) query time and O(dn + n) space, where ρ ≤ O((ln c)/c). This improves upon the previous best upper bound ρ ≤ 1/c by Datar et al. (SOCG 2004), and is close to the lower bound ρ ≥ 1/c by O’Donnell, Wu and Zhou (ITCS 2011). T...

Approximate Nearest Neighbor Search Project Report

Hardness of Approximate Nearest Neighbor Search

We prove conditional near-quadratic running time lower bounds for approximate Bichromatic Closest Pair with Euclidean, Manhattan, Hamming, or edit distance. Specifically, unless the Strong Exponential Time Hypothesis (SETH) is false, for every δ > 0 there exists a constant ε > 0 such that computing a (1 + ε)-approximation to the Bichromatic Closest Pair requires Ω (