Principal component analysis (PAC)

The main purpose of principal component analysis(PCA) is to reduce the dimensionality from p to d, where d<p, while at the same time accounting for as much of the variation in the original data set as possible. With PCA, we transform the data to a new set of coordinates or variables that are a linear combination of the original variables. In addition, the observations in the new principal component space are uncorrelated. The hope is that we can gain information and understanding of the data by looking at the observations in the new space.