Boaz Nadler, Finite Sample Approximation Results for Principal Component Analysis: A Matrix Perturbation Approach, The Annals of Statistics, Vol. 36, No. 6, High Dimensional Inference and Random ...

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

The purpose of principal component analysis is to derive a small number of independent linear combinations (principal components) of a set of variables that retain as much of the information in the ...

The separation of morphometric variation into a component related to size and other components associated with shape is of considerable interest and has generated much discussion in the literature.

Principal component analysis summarizes high dimensional data into a few dimensions. Each dimension is called a principal component and represents a linear combination of the variables.