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 ...

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 ...

This article presents and compares two approaches of principal component (PC) analysis for two-dimensional functional data on a possibly irregular domain. The first approach applies the singular value ...

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 ...

Using Recurrent Neural Networks to Extract High-Quality Information From Lung Cancer Screening Computerized Tomography Reports for Inter-Radiologist Audit and Feedback Quality Improvement Patients who ...

The Heisenberg uncertainty principle, which has origins in physics, "states that there is a limit to the precision with which certain pairs of physical properties of a particle, such as position and ...