  Real data are often with multiple modalities or from multiple heterogeneous
sources, thus forming so-called multi-view data, which receives more and more
attentions in machine learning. Multi-view clustering (MVC) becomes its
important paradigm. In real-world applications, some views often suffer from
instances missing. Clustering on such multi-view datasets is called incomplete
multi-view clustering (IMC) and quite challenging. To date, though many
approaches have been developed, most of them are offline and have high
computational and memory costs especially for large scale datasets. To address
this problem, in this paper, we propose an One-Pass Incomplete Multi-view
Clustering framework (OPIMC). With the help of regularized matrix factorization
and weighted matrix factorization, OPIMC can relatively easily deal with such
problem. Different from the existing and sole online IMC method, OPIMC can
directly get clustering results and effectively determine the termination of
iteration process by introducing two global statistics. Finally, extensive
experiments conducted on four real datasets demonstrate the efficiency and
effectiveness of the proposed OPIMC method.
