  Multi-view subspace clustering has been applied to applications such as image
processing and video surveillance, and has attracted increasing attention. Most
existing methods learn view-specific self-representation matrices, and
construct a combined affinity matrix from multiple views. The affinity
construction process is time-consuming, and the combined affinity matrix is not
guaranteed to reflect the whole true subspace structure. To overcome these
issues, the Latent Complete Row Space Recovery (LCRSR) method is proposed.
Concretely, LCRSR is based on the assumption that the multi-view observations
are generated from an underlying latent representation, which is further
assumed to collect the authentic samples drawn exactly from multiple subspaces.
LCRSR is able to recover the row space of the latent representation, which not
only carries complete information from multiple views but also determines the
subspace membership under certain conditions. LCRSR does not involve the graph
construction procedure and is solved with an efficient and convergent
algorithm, thereby being more scalable to large-scale datasets. The
effectiveness and efficiency of LCRSR are validated by clustering various kinds
of multi-view data and illustrated in the background subtraction task.
