  We consider the problem of learning a linear factor model. We propose a
regularized form of principal component analysis (PCA) and demonstrate through
experiments with synthetic and real data the superiority of resulting estimates
to those produced by pre-existing factor analysis approaches. We also establish
theoretical results that explain how our algorithm corrects the biases induced
by conventional approaches. An important feature of our algorithm is that its
computational requirements are similar to those of PCA, which enjoys wide use
in large part due to its efficiency.
