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.