  In the era of big data, it is common to have data with multiple modalities or
coming from multiple sources, known as "multi-view data". Multi-view clustering
provides a natural way to generate clusters from such data. Since different
views share some consistency and complementary information, previous works on
multi-view clustering mainly focus on how to combine various numbers of views
to improve clustering performance. However, in reality, each view may be
incomplete, i.e., instances missing in the view. Furthermore, the size of data
could be extremely huge. It is unrealistic to apply multi-view clustering in
large real-world applications without considering the incompleteness of views
and the memory requirement. None of previous works have addressed all these
challenges simultaneously. In this paper, we propose an online multi-view
clustering algorithm, OMVC, which deals with large-scale incomplete views. We
model the multi-view clustering problem as a joint weighted nonnegative matrix
factorization problem and process the multi-view data chunk by chunk to reduce
the memory requirement. OMVC learns the latent feature matrices for all the
views and pushes them towards a consensus. We further increase the robustness
of the learned latent feature matrices in OMVC via lasso regularization. To
minimize the influence of incompleteness, dynamic weight setting is introduced
to give lower weights to the incoming missing instances in different views.
More importantly, to reduce the computational time, we incorporate a faster
projected gradient descent by utilizing the Hessian matrices in OMVC. Extensive
experiments conducted on four real data demonstrate the effectiveness of the
proposed OMVC method.
