  The topic of this tutorial is Least Squares Sparse Principal Components
Analysis (LS SPCA) which is a simple method for computing approximated
Principal Components which are combinations of only a few of the observed
variables. Analogously to Principal Components, these components are
uncorrelated and sequentially best approximate the dataset. The derivation of
LS SPCA is intuitive for anyone familiar with linear regression. Since LS SPCA
is based on a different optimality from other SPCA methods and does not suffer
from their serious drawbacks. I will demonstrate on two datasets how useful and
parsimonious sparse PCs can be computed. An R package for computing LS SPCA is
available for download.
