  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 outlier model where each column of the data matrix is either an inlier or
an outlier. Most of the existing methods for this 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 d)
can handle structured and unstructured outliers. Further, analytical guarantees
are derived for outlier identification and the performance of the algorithm is
compared with the existing state of the art methods in both real and synthetic
data for various outlier structures.
