Research Output

Parallelization of the nearest-neighbour search and the cross-validation error evaluation for the kernel weighted k-nn algorithm applied to large data dets in matlab

  The kernel weighted k-nearest neighbours (KWKNN) algorithm is an efficient kernel regression method that achieves competitive results with lower computational complexity than Least-Squares Support Vector Machines and Gaussian Processes. This paper presents the parallel implementation on a cluster platform of the sequential KWKNN implemented in Matlab. This implies both the parallelization of the k nearest-neighbour search and the evaluation of the cross-validation error on a large distributed data set. The results demonstrate the good performances of the implementation.

  • Date:

    07 August 2009

  • Publication Status:

    Published

  • Publisher

    Institute of Electrical & Electronics Engineers (IEEE)

  • DOI:

    10.1109/hpcsim.2009.5192804

  • Library of Congress:

    QA Mathematics

  • Dewey Decimal Classification:

    518 Numerical analysis

Citation

Rubio, G., Guillen, A., Pomares, H., Rojas, I., Paechter, B., Glosekotter, P. & Torres-Ceballos, C. I. (2009). Parallelization of the nearest-neighbour search and the cross-validation error evaluation for the kernel weighted k-nn algorithm applied to large data dets in matlab. In HPCS '09. International Conference on High Performance Computing & Simulation, 2009.. doi:10.1109/hpcsim.2009.5192804. ISBN 978-1-4244-4906-4

Authors

Keywords

Large Scale Scientific Computing, Libraries and Programming Environments, Languages, Message Passing, Matlab

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