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 ...

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, ...

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 ...

Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear combination of all the ...

Compositional data, consisting of vectors of proportions, have proved difficult to handle statistically because of the awkward constraint that the components of each vector must sum to unity. Moreover ...