  Subspace clustering methods have been widely studied recently. When the
inputs are 2-dimensional (2D) data, existing subspace clustering methods
usually convert them into vectors, which severely damages inherent structures
and relationships from original data. In this paper, we propose a novel
subspace clustering method for 2D data. It directly uses 2D data as inputs such
that the learning of representations benefits from inherent structures and
relationships of the data. It simultaneously seeks image projection and
representation coefficients such that they mutually enhance each other and lead
to powerful data representations. An efficient algorithm is developed to solve
the proposed objective function with provable decreasing and convergence
property. Extensive experimental results verify the effectiveness of the new
method.
