Enhanced Approximate Nearest Neighbor via Local Area Focused Search.

Technical Report · Wed Feb 01 04:00:00 EST 2017

DOI:https://doi.org/10.2172/1367491· OSTI ID:1367491

Gonzales, Antonio ^[1]; Blazier, Nicholas Paul ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Approximate Nearest Neighbor (ANN) algorithms are increasingly important in machine learning, data mining, and image processing applications. There is a large family of space- partitioning ANN algorithms, such as randomized KD-Trees, that work well in practice but are limited by an exponential increase in similarity comparisons required to optimize recall. Additionally, they only support a small set of similarity metrics. We present Local Area Fo- cused Search (LAFS), a method that enhances the way queries are performed using an existing ANN index. Instead of a single query, LAFS performs a number of smaller (fewer similarity comparisons) queries and focuses on a local neighborhood which is refined as candidates are identified. We show that our technique improves performance on several well known datasets and is easily extended to general similarity metrics using kernel projection techniques.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 1367491
• Report Number(s): SAND--2017-1704; 654182
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING