Work

K-nearest Neighbour Search Using Random Projection Forests

Year: 2025

Type: article

Abstract: The K-NN is frequently implemented in machine learning and pattern recognition applications. Non-parametric methods are employed to evaluate data, classify objects, identify outliers, and offer recommendations. Using a query point and a distance metric (such as Manhattan or Euclidean distance), the K-NN algorithm can identify the 'k' closest points in your dataset. This straightforward concept is both effective and straightforward. K-NN is inefficient on large or high-dimensional datasets, despite its usefulness, as it is required to compare each query point with every dataset point.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.16811227

Author: EMERGING TRENDS IN DIGITAL TRANSFORMATION

Language: English

Topic: Advanced Image and Video Retrieval Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6949682049

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