Accepted Paper: Latent Multi-view Semi-Supervised Classification

Back to list of accepted papers

Authors

Xiaofan Bo (University of Electronic Science and Technology); Zhao Kang (University of Electronic Science and Technology of China); Zhitong Zhao (University of Electronic Science and Technology); yuanzhang su (University of Electronic Science and Technology); Wenyu Chen (University of Electronic Science and Technology of China)

Abstract

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.