  We consider a decomposition method for compressive streaming data in the
context of online compressive Robust Principle Component Analysis (RPCA). The
proposed decomposition solves an $n$-$\ell_1$ cluster-weighted minimization to
decompose a sequence of frames (or vectors), into sparse and low-rank
components, from compressive measurements. Our method processes a data vector
of the stream per time instance from a small number of measurements in contrast
to conventional batch RPCA, which needs to access full data. The $n$-$\ell_1$
cluster-weighted minimization leverages the sparse components along with their
correlations with multiple previously-recovered sparse vectors. Moreover, the
proposed minimization can exploit the structures of sparse components via
clustering and re-weighting iteratively. The method outperforms the existing
methods for both numerical data and actual video data.
