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Updated: May 29, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Randomized approximate nearest neighbors algorithm.

Peter Wilcox Jones1, Andrei Osipov, Vladimir Rokhlin

  • 1Yale University, A. K. Watson Hall, 51 Prospect Street, New Haven, CT 06511, USA.

Proceedings of the National Academy of Sciences of the United States of America
|September 3, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a randomized algorithm for finding approximate nearest neighbors in high-dimensional data. The method efficiently identifies neighbors using random rotations and divide-and-conquer, suitable for large datasets.

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Last Updated: May 29, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Area of Science:

  • Computer Science
  • Machine Learning
  • Computational Geometry

Background:

  • The approximate nearest neighbor (ANN) problem is fundamental in high-dimensional data analysis.
  • Efficiently finding nearest neighbors is crucial for many machine learning algorithms.
  • Existing methods often struggle with scalability in high dimensions.

Purpose of the Study:

  • To develop a novel randomized algorithm for the approximate nearest neighbor problem in d-dimensional Euclidean space.
  • To provide a data structure for rapid k-nearest neighbor queries.
  • To analyze the algorithm's performance and computational complexity.

Main Methods:

  • A randomized, iterative algorithm employing random rotations and a divide-and-conquer strategy.
  • Incorporation of a local graph search component.
  • Analysis of the algorithm's time and memory complexity.

Main Results:

  • The algorithm achieves efficient approximate nearest neighbor search with specified parameters k and T iterations.
  • Running time is proportional to T·N·(d·(log d) + k·(d + log k)·(log N)) + N·k(2)·(d + log k).
  • Memory requirements are of the order N·(d + k), with query costs detailed.

Conclusions:

  • The presented randomized algorithm offers an efficient solution for the approximate nearest neighbor problem.
  • The associated data structure enables fast k-nearest neighbor searches for arbitrary points.
  • The scheme's performance is demonstrated through analysis and numerical examples.