  Principal components analysis (PCA) is a classical method for the reduction
of dimensionality of data in the form of n observations (or cases) of a vector
with p variables. For a simple model of factor analysis type, it is proved that
ordinary PCA can produce a consistent (for n large) estimate of the principal
factor if and only if p(n) is asymptotically of smaller order than n.
  There may be a basis in which typical signals have sparse representations:
most co-ordinates have small signal energies. If such a basis (e.g. wavelets)
is used to represent the signals, then the variation in many coordinates is
likely to be small. Consequently, we study a simple "sparse PCA" algorithm:
select a subset of coordinates of largest variance, estimate eigenvectors from
PCA on the selected subset, threshold and reexpress in the original basis. We
illustrate the algorithm on some exercise ECG data, and prove that in a single
factor model, under an appropriate sparsity assumption, it yields consistent
estimates of the principal factor.
