Sparse principal component analysis (SPCA) extends classical principal component analysis to settings where the number of variables greatly exceeds the number of observations. By imposing sparsity ...
Seasonally frozen soils are exposed to freeze‒thaw cycles every year, leading to mechanical property deterioration. To reasonably describe the deterioration of soil under different conditions, machine ...
PCA is an important tool for dimensionality reduction in data science and to compute grasp poses for robotic manipulation from point cloud data. PCA can also directly used within a larger machine ...
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