This is a small but efficient tool to perform K-nearest neighbor search, which has wide Science and Engineering applications, such as pattern recognition, data mining and signal processing.
The code was initially implemented through vectorization. After discussions with John D'Errico, I realized that my algorithm will suffer numerical accurancy problem for data with large values. Then, after trying several approaches, I found simple loops with JIT acceleration is the most efficient solution. Now, the performance of the code is comparable with kd-tree even the latter is coded in a mex file.
The code is very simple, hence is also suitable for beginner to learn knn search.
Yi Cao (2024). Efficient K-Nearest Neighbor Search using JIT (https://www.mathworks.com/matlabcentral/fileexchange/19345-efficient-k-nearest-neighbor-search-using-jit), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
Inspired: Compute surface variation
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