  Principal component analysis (PCA) is known to be sensitive to outliers, so
that various robust PCA variants were proposed in the literature. A recent
model, called REAPER, aims to find the principal components by solving a convex
optimization problem. Usually the number of principal components must be
determined in advance and the minimization is performed over symmetric positive
semi-definite matrices having the size of the data, although the number of
principal components is substantially smaller. This prohibits its use if the
dimension of the data is large which is often the case in image processing.
  In this paper, we propose a regularized version of REAPER which enforces the
sparsity of the number of principal components by penalizing the nuclear norm
of the corresponding orthogonal projector. This has the advantage that only an
upper bound on the number of principal components is required. Our second
contribution is a matrix-free algorithm to find a minimizer of the regularized
REAPER which is also suited for high dimensional data. The algorithm couples a
primal-dual minimization approach with a thick-restarted Lanczos process. As a
side result, we discuss the topic of the bias in robust PCA. Numerical examples
demonstrate the performance of our algorithm.
