Near-optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
Abstract¶
In this article, we give an overview of efficient algorithms for the approximate and exact nearest neighbor problem. The goal is to preprocess a dataset of objects (e.g., images) so that later, given a new query object, one can quickly return the dataset object that is most similar to the query. The problem is of significant interest in a wide variety of areas.
Summary¶
Develops near-optimal locality-sensitive hashing schemes for approximate nearest-neighbor search in high-dimensional Euclidean spaces. The work improved the theoretical query-time exponent and influenced later practical ANN data structures.
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