A distributed kernel summation framework for general‐dimension machine learning

DOI10.1002/sam.11207OpenAlexW1908407117MaRDI QIDQ4969920

Richard Vuduc, Piyush Sao, Alexander Gray, Dongryeol Lee

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/sam.11207

zbMATH Keywords

kernel methodsnonparametric methodsCUDAGPGPUparallel machine learningparallel multidimensional trees

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Related Items

A hybrid stochastic interpolation and compression method for kernel matrices ⋮ An \(O(N \log N)\) hierarchical random compression method for kernel matrices by sampling partial matrix entries ⋮ Fast and Stable Multivariate Kernel Density Estimation by Fast Sum Updating ⋮ ASKIT: An Efficient, Parallel Library for High-Dimensional Kernel Summations ⋮ ASKIT: Approximate Skeletonization Kernel-Independent Treecode in High Dimensions

Uses Software

• Boost C++ Libraries
• Armadillo
• PetFMM
• Boost

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A kernel-independent adaptive fast multipole algorithm in two and three dimensions
• Load balancing and data locality in adaptive hierarchical \(N\)-body methods: Barnes-Hut, fast multipole, and radiosity
• PetFMM-A dynamically load-balancing parallel fast multipole library
• Multidimensional binary search trees used for associative searching
• A decomposition of multidimensional point sets with applications to k -nearest-neighbors and n -body potential fields
• On Estimation of a Probability Density Function and Mode
• The Fast Gauss Transform
• A fast algorithm for particle simulations

This page was built for publication: A distributed kernel summation framework for general‐dimension machine learning