Parallel Algorithms for Nearest Neighbor Search Problems in High Dimensions

DOI10.1137/15M1026377zbMath1349.68231MaRDI QIDQ2830635

George Biros, Bo Xiao

Publication date: 28 October 2016

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

zbMATH Keywords

parallel algorithmsdata analysismachine learningcomputational statisticstree codesnearest neighbor algorithms

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Parallel algorithms in computer science (68W10) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Distributed algorithms (68W15)

Related Items (1)

Training very large scale nonlinear SVMs using alternating direction method of multipliers coupled with the hierarchically semi-separable kernel approximations

Uses Software

• UCI-ml
• MLPACK
• ANN
• FLANN
• ASKIT
• E2LSH
• HykSort

Cites Work

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• A non-linear dimension reduction methodology for generating data-driven stochastic input models
• Satisfying general proximity/similarity queries with metric trees
• Foundations of multidimensional and metric data structures.
• Parallel Processing for Scientific Computing
• Finding nearest neighbors in growth-restricted metrics
• A Branch and Bound Algorithm for Computing k-Nearest Neighbors
• An Algorithm for Finding Best Matches in Logarithmic Expected Time
• Separators for sphere-packings and nearest neighbor graphs
• ASKIT: Approximate Skeletonization Kernel-Independent Treecode in High Dimensions
• The effectiveness of lloyd-type methods for the k-means problem
• The elements of statistical learning. Data mining, inference, and prediction

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