Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions

Author:

Andoni Alexandr,Indyk Piotr1

Affiliation:

1. Massachusetts Institute of Technology, Cambridge, MA

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.

Funder

National Science Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference37 articles.

1. Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform

2. Andoni A. and Indyk P. 2004. E2lsh: Exact Euclidean localitysensitive hashing. http://web.mit.edu/andoni/www/LSH/. Andoni A. and Indyk P. 2004. E2lsh: Exact Euclidean localitysensitive hashing. http://web.mit.edu/andoni/www/LSH/.

3. Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions

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