  Spectral clustering is an effective methodology for unsupervised learning.
Most traditional spectral clustering algorithms involve a separate two-step
procedure and apply the transformed new representations for the final
clustering results. Recently, much progress has been made to utilize the
non-negative feature property in real-world data and to jointly learn the
representation and clustering results. However, to our knowledge, no previous
work considers a unified model that incorporates the important multi-view
information with those properties, which severely limits the performance of
existing methods. In this paper, we formulate a novel clustering model, which
exploits the non-negative feature property and, more importantly, incorporates
the multi-view information into a unified joint learning framework: the unified
multi-view orthonormal non-negative graph based clustering framework
(Umv-ONGC). Then, we derive an effective three-stage iterative solution for the
proposed model and provide analytic solutions for the three sub-problems from
the three stages. We also explore, for the first time, the multi-model
non-negative graph-based approach to clustering data based on deep features.
Extensive experiments on three benchmark data sets demonstrate the
effectiveness of the proposed method.
