Algorithm

Process (computing)

Multivariate statistics

Principal component analysis

Curse of dimensionality

Operating system

Data mining

Computer science

Exploratory data analysis

Pattern recognition (psychology)

Artificial intelligence

Cluster analysis

Machine learning

Proceedings of the IEEE

The papers in this special issue introduce the reader to the theory, algorithms, and applications of principal component analysis (PCA) and its many extensions. The aim of PCA is to reduce the dimensionality of multivariate data while preserving as much of the relevant information as possible. It is often the first step in various types of exploratory data analysis, predictive modeling, and classification and clustering tasks, and finds applications in biomedical imaging, computer vision, process fault detection, recommendation systems’ design, and many more domains.

Rethinking PCA for Modern Data Sets: Theory, Algorithms, and Applications [Scanning the Issue]