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 ...

Principal component analysis is a widely used technique that provides an optimal lower-dimensional approximation to multivariate or functional datasets. These approximations can be very useful in ...

Sparse Principal Component Analysis (sparse PCA) represents a significant advance in the field of dimensionality reduction for high-dimensional data. Unlike conventional Principal Component Analysis ...

Results are presented from Principal Components Analysis of three data matrices (pollen concentration per c.c., percentages of total pollen, and analyses for eight or nine chemical elements) of c.

Bruce H. Lipnick is Founder, Chief Executive Officer and Chairman of the Board of Directors of Asset Alliance Corporation (“Asset Alliance”). He is also Chairman and Chief Executive Officer of Asset ...