Principal Component Local Mean Clustering of Spatial Data

版本 1.0.0.1 (13.0 KB) 作者: Carlo Grillenzoni
2D and 3D marked point clouds (as earthquake hypocenters) are clustered as curves and surfaces using local means and PC of cov. matrices.
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2D and 3D marked point clouds (as earthquake hypocenters) are clustered as principal curves and principal surfaces (to detect tectonic faults), using local means and principal components of the local covariance matrices of the points. The toolbox provides basic estimation algorithms in 2D and 3D and methods for tentative automatic hyperparameter selection, such as the local sample size (n nearest neighbors) and the number if iterations.

引用格式

Carlo Grillenzoni (2024). Principal Component Local Mean Clustering of Spatial Data (https://www.mathworks.com/matlabcentral/fileexchange/121747-principal-component-local-mean-clustering-of-spatial-data), MATLAB Central File Exchange. 检索来源 .

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版本 已发布 发行说明
1.0.0.1