  Multi-view data are becoming common in real-world modeling tasks and many
multi-view data clustering algorithms have thus been proposed. The existing
algorithms usually focus on the cooperation of different views in the original
space but neglect the influence of the hidden information among these different
visible views, or they only consider the hidden information between the views.
The algorithms are therefore not efficient since the available information is
not fully excavated, particularly the otherness information in different views
and the consistency information between them. In practice, the otherness and
consistency information in multi-view data are both very useful for effective
clustering analyses. In this study, a Multi-View clustering algorithm developed
with the Cooperation of Visible and Hidden views, i.e., MV-Co-VH, is proposed.
The MV-Co-VH algorithm first projects the multiple views from different visible
spaces to the common hidden space by using the non-negative matrix
factorization (NMF) strategy to obtain the common hidden view data.
Collaborative learning is then implemented in the clustering procedure based on
the visible views and the shared hidden view. The results of extensive
experiments on UCI multi-view datasets and real-world image multi-view datasets
show that the clustering performance of the proposed algorithm is competitive
with or even better than that of the existing algorithms.
