KMeans_SPD_Matrices​.zip

K-Means Clustering for a Population of Symmetric Positive-Definite (SPD) Matrices
780 Downloads
Updated 24 Apr 2014

View License

This package contains 8 different K-means clustering techniques, applicable to a group of Symmetric Positive Definite (SPD) matrices. The algorithms are different based on (1) the distance/divergence measures used to compare the samples to the cluster centers, and (2) the corresponding mean computation technique, i.e., incremental vs. non-incremental.
The dissimilarity measures used here are: (1) natural geodesic distance on P(n), (2) Stein distance, (3) LogEuclidean distance and (4) Kullback-Leibler divergence.
Mean computation methods are provided in both the incremental and non-incremental frameworks, based on the aforementioned dissimilarity measures.

If you use this software please cite the following papers:

[1] Guang Cheng, Hesamoddin Salehian, Baba C. Vemuri, €œEfficient Recursive Algorithms for Computing the Mean Diffusion Tensor and Applications to DTI Segmentation, European Conference on Computer Vision (ECCV) 2012.

[2] Jeffrey Ho, Guang Cheng, Hesamoddin Salehian, Baba C. Vemuri, €œRecursive Karcher Expectation Estimators And Geometric Law of Large Numbers, International Conference on Artificial Intelligence and Statistics (AISTATS) 2013.

[3] Hesamoddin Salehian, Guang Cheng, Baba C. Vemuri, Jeffrey Ho, Recursive Estimation of the Stein Center of SPD Matrices & its Applications€, International Conference on Computer Vision (ICCV) 2013.

Cite As

Hesamoddin (2024). KMeans_SPD_Matrices.zip (https://www.mathworks.com/matlabcentral/fileexchange/46343-kmeans_spd_matrices-zip), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2012b
Compatible with any release
Platform Compatibility
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
Version Published Release Notes
1.1.0.0

Updated the description.

1.0.0.0