This function computes pairwise distance between two sample sets and produce a matrix of square of Euclidean or Mahalanobis distances. The code is fully optimized by vectorization. Therefore it is much faster than the built-in function pdist.
When two matrices A and B are provided as input, this function computes the square Euclidean distances between them. If an extra positive definite matrix M is provided, it computes Mahalanobis distances.
If only one matrix A is provided, the function computes pairwise square Euclidean distances between vectors in A. In this case, it is equivalent to the square of pdist function in matlab statistics toolbox but much faster.
Detail explanation can be found in following blog post:
This function is now a part of the PRML toolbox (http://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox).
Mo Chen (2023). Pairwise Distance Matrix (https://www.mathworks.com/matlabcentral/fileexchange/24599-pairwise-distance-matrix), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
Inspired by: Pattern Recognition and Machine Learning Toolbox
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!Start Hunting!
update title and description
remove any redundant error check
update to support Mahalanobis distance. fix a bug for one dimensional case.
Add a centerization step for robustness purpose. Split the code for different number of input for efficiency purpose. Update comments.
update the description