Latent Multi-view Semi-Supervised Classification
This work addresses the challenge of leveraging complementary information in multi-view data for semi-supervised classification, which is an incremental improvement over existing methods.
The paper tackles the problem of multi-view semi-supervised classification by proposing a novel method that learns an underlying latent representation to integrate complementary information from multiple views, resulting in more accurate and robust graph construction and label propagation, with experimental validation on benchmark datasets.
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.