  Multi-view subspace clustering always performs well in high-dimensional data
analysis, but is sensitive to the quality of data representation. To this end,
a two stage fusion strategy is proposed to embed representation learning into
the process of multi-view subspace clustering. This paper first propose a novel
matrix factorization method that can separate the coupling consistent and
complementary information from observations of multiple views. Based on the
obtained latent representations, we further propose two subspace clustering
strategies: feature-level fusion and subspace-level hierarchical strategy.
Feature-level method concatenates all kinds of latent representations from
multiple views, and the original problem therefore degenerates to a single-view
subspace clustering process. Subspace-level hierarchical method performs
different self-expressive reconstruction processes on the corresponding
complementary and consistent latent representations coming from each view, i.e.
the prior constraints imposed on different types of subspace representations
are related to the appropriate input factors. Finally, extensive experimental
results on real-world datasets demonstrate the superiority of our proposed
methods by comparing against some state-of-the-art subspace clustering
algorithms.
