  To explore underlying complementary information from multiple views, in this
paper, we propose a novel Latent Multi-view Semi-Supervised Classification
(LMSSC) method. Unlike most existing multi-view semi-supervised classification
methods that learn the graph using original features, our method seeks an
underlying latent representation and performs graph learning and label
propagation based on the learned latent representation. With the
complementarity of multiple views, the latent representation could depict the
data more comprehensively than every single view individually, accordingly
making the graph more accurate and robust as well. Finally, LMSSC integrates
latent representation learning, graph construction, and label propagation into
a unified framework, which makes each subtask optimized. Experimental results
on real-world benchmark datasets validate the effectiveness of our proposed
method.
