KMeans_SPD_Matrices.zip
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, Efficient 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, Recursive 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.
Zitieren als
Hesamoddin (2024). KMeans_SPD_Matrices.zip (https://www.mathworks.com/matlabcentral/fileexchange/46343-kmeans_spd_matrices-zip), MATLAB Central File Exchange. Abgerufen .
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KMeans_SPD_Matrices/
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Version | Veröffentlicht | Versionshinweise | |
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1.1.0.0 | Updated the description. |
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1.0.0.0 |