The Data Science Lab Anomaly Detection Using Principal Component Analysis (PCA) The main advantage of using PCA for anomaly detection, compared to alternative techniques such as a neural autoencoder, ...

Principal component analysis (PCA) is a classical machine learning technique. The goal of PCA is to transform a dataset into one with fewer columns. This is called dimensionality reduction. The ...

Principal component analysis is often incorporated into genome-wide expression studies, but what is it and how can it be used to explore high-dimensional data?

9400 11.4 4000 100 13000 ; proc factor data=SocioEconomics simple corr; title3 'Principal Component Analysis'; run; There are two large eigenvalues, 2.8733 and 1.7967, which together account for 93.4% ...

Using the two principal components of a point cloud for robotic grasping as an example, we will derive a numerical implementation of the PCA, which will help to understand what PCA is and what it does ...

The data in this example are 1985 -1986 preseason rankings of 35 college basketball teams by 10 different news services. The services do not all rank the same teams or the same number of teams, so ...

Functional data analysis on nonlinear manifolds has drawn recent interest. Sphere-valued functional data, which are encountered, for example, as movement trajectories on the surface of the earth are ...