  Robust PCA, the problem of PCA in the presence of outliers has been
extensively investigated in the last few years. Here we focus on Robust PCA in
the column sparse outlier model. The existing methods for column sparse outlier
model assumes either the knowledge of the dimension of the lower dimensional
subspace or the fraction of outliers in the system. However in many
applications knowledge of these parameters is not available. Motivated by this
we propose a parameter free outlier identification method for robust PCA which
a) does not require the knowledge of outlier fraction, b) does not require the
knowledge of the dimension of the underlying subspace, c) is computationally
simple and fast. Further, analytical guarantees are derived for outlier
identification and the performance of the algorithm is compared with the
existing state of the art methods.
