  Dimension reduction is widely regarded as an effective way for decreasing the
computation, storage and communication loads of data-driven intelligent
systems, leading to a growing demand for statistical methods that allow
analysis (e.g., clustering) of compressed data. We therefore study in this
paper a novel problem called compressive robust subspace clustering, which is
to perform robust subspace clustering with the compressed data, and which is
generated by projecting the original high-dimensional data onto a
lower-dimensional subspace chosen at random. Given only the compressed data and
sensing matrix, the proposed method, row space pursuit (RSP), recovers the
authentic row space that gives correct clustering results under certain
conditions. Extensive experiments show that RSP is distinctly better than the
competing methods, in terms of both clustering accuracy and computational
efficiency.
