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 ...

Sparse Principal Component Analysis (sparse PCA) represents a significant advance in the field of dimensionality reduction for high-dimensional data. Unlike conventional Principal Component Analysis ...

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 ...

WEST LAFAYETTE, Ind. — In recent years, the market for direct-to-consumer genetic testing has exploded. The number of people who used at-home DNA tests more than doubled in 2017, most of them in the U ...

This tool allows you interactively select solvents based upon the Principal Component Analysis (PCA) of the solvent's physical properties. Solvents which are close to each other in the map have ...