Chipman, Hugh; Gu, Hong - In: Journal of Applied Statistics 32 (2005) 9, pp. 969-987
The analysis of high-dimensional data often begins with the identification of lower dimensional subspaces. Principal component analysis is a dimension reduction technique that identifies linear combinations of variables along which most variation occurs or which best “reconstruct” the...