  Latent multi-view subspace clustering has been demonstrated to have desirable
clustering performance. However, the original latent representation method
vertically concatenates the data matrices from multiple views into a single
matrix along the direction of dimensionality to recover the latent
representation matrix, which may result in an incomplete information recovery.
To fully recover the latent space representation, we in this paper propose an
Enhanced Latent Multi-view Subspace Clustering (ELMSC) method. The ELMSC method
involves constructing an augmented data matrix that enhances the representation
of multi-view data. Specifically, we stack the data matrices from various views
into the block-diagonal locations of the augmented matrix to exploit the
complementary information. Meanwhile, the non-block-diagonal entries are
composed based on the similarity between different views to capture the
consistent information. In addition, we enforce a sparse regularization for the
non-diagonal blocks of the augmented self-representation matrix to avoid
redundant calculations of consistency information. Finally, a novel iterative
algorithm based on the framework of Alternating Direction Method of Multipliers
(ADMM) is developed to solve the optimization problem for ELMSC. Extensive
experiments on real-world datasets demonstrate that our proposed ELMSC is able
to achieve higher clustering performance than some state-of-art multi-view
clustering methods.
