A good way to see where this article is headed is to take a look at the screen shot of a demo program shown in Figure 1. The demo sets up a dummy dataset of six items: [ 5.1 3.5 1.4 0.2] [ 5.4 3.9 1.7 ...

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 ...

We propose a semiparametric method for conducting scale-invariant sparse principal component analysis (PCA) on high-dimensional non-Gaussian data. Compared with sparse PCA, our method has a weaker ...

In the context of principal components analysis (PCA), the bootstrap is commonly applied to solve a variety of inference problems, such as constructing confidence intervals for the eigenvalues of the ...

Inside living cells, mitochondria divide, lysosomes travel, and synaptic vesicles pulse—all in three dimensions (3Ds) and constant motion. Capturing these events with clarity is vital not just for ...