The main advantage of using PCA for anomaly detection, compared to alternative techniques such as a neural autoencoder, is simplicity -- assuming you have a function that computes eigenvalues and ...

Moreover the efficacy of the data interpretation tool principal component analysis (PCA) is debated for potential applications in polymer characterisation and polymer degradation.

Dimension Reduction and Classification Using PCA, Factor Analysis and Discriminant Functions - A Short Overview Course Topics Tuesday, October 28: Often researchers are faced with data in very high ...

Researchers at Nanjing University of Science and Technology (NJUST) developed PCA-3DSIM, a mathematically grounded ...

Plant Ecology, Vol. 216, No. 5, Special Issue: Statistical Analysis of Ecological Communities: Progress, Status, and Future Directions (MAY 2015), pp. 657-667 (11 pages) Principal component analysis ...

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 ...

Simplify complex datasets using Principal Component Analysis (PCA) in Python. Great for dimensionality reduction and visualization.

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 ...