  In this paper we analyze approximate methods for undertaking a principal
components analysis (PCA) on large data sets. PCA is a classical dimension
reduction method that involves the projection of the data onto the subspace
spanned by the leading eigenvectors of the covariance matrix. This projection
can be used either for exploratory purposes or as an input for further
analysis, e.g. regression. If the data have billions of entries or more, the
computational and storage requirements for saving and manipulating the design
matrix in fast memory is prohibitive. Recently, the Nystr\"om and
column-sampling methods have appeared in the numerical linear algebra community
for the randomized approximation of the singular value decomposition of large
matrices. However, their utility for statistical applications remains unclear.
We compare these approximations theoretically by bounding the distance between
the induced subspaces and the desired, but computationally infeasible, PCA
subspace. Additionally we show empirically, through simulations and a real data
example involving a corpus of emails, the trade-off of approximation accuracy
and computational complexity.

Machine Learning;Computation