A Novel Weighted Hybrid Multi-View Fusion Algorithm for Semi-Supervised Classification
Song Wang, Xin Guo, Yun Tie, Lin Qi, Ling Guan · 2019
Semi-supervised learning aims to improve the learning performance with very limited label information. To dig more available information from the collected data, we propose a weighted hybrid multi-view feature fusion approach for semi-supervised classification problem. Specifically, under the rank consistency constraint for labels predicted by view-specific learners, the proposed method estimates the optimal fusion weight for each learner to balance the incomparable square losses on different views. In this case, the learners with more powerful prediction capability are pushed to have higher weights during the fusion process. Experimental results on 6 real-world datasets demonstrate the effectiveness of the proposed technique.