Along with the use of principal component regression there appears to have been a growth in the misconception that the principal components with small eigenvalues will very rarely be of any use in ...

Jingyi K. TAY, Jerome FRIEDMAN, Robert TIBSHIRANI, Principal component-guided sparse regression, The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 49, No. 4 ...

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 ...

Principal Component Analysis (PCA) is widely used in data analysis and machine learning to reduce the dimensionality of a dataset. The goal is to find a set of linearly uncorrelated (orthogonal) ...

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?

The Data Science Lab Principal Component Analysis (PCA) from Scratch Using the Classical Technique with C# Transforming a dataset into one with fewer columns is more complicated than it might seem, ...