This paper proposes a weighted hybrid multi-view fusion method for the semi-supervised classification problem. Instead of getting access to the features from different views directly, this method utilizes the square losses of the multi-view classifiers to exploit the between-view relationship, which preserves the privacy of data. Considering the different prediction capability of classifiers on multiple views, an objective function with the constraint of rank consistency is constructed to weight view-specific learners adaptively, where the constraint makes each view-specific learner improve its performance by exploring the predicted results of other learners. Furthermore, an iterative algorithm based on the Variant Alternating Splitting Augmented Lagrangian Method (VASALM) and the quadratic programming method is developed to optimize the objective function. Experimental results on different real-world datasets demonstrate the effectiveness of the proposed method for multi-view learning. The experiments also analyze parameter sensitivity and convergency of the optimization algorithm.

针对半监督分类问题，提出了一种加权混合多视图融合方法。该方法不是直接从不同的视图访问要素，而是利用多视图分类器的平方损失来利用视图间关系，从而保留了数据的隐私性。考虑到分类器对多个视图的预测能力不同，构造了具有等级一致性约束的目标函数，以自适应地加权视图特定的学习者，该约束使每个视图特定的学习者通过探索其他学习者的预测结果来提高其性能。 。此外，提出了基于变分交替分裂拉格朗日方法（VASALM）和二次规划方法的迭代算法，以优化目标函数。在不同的现实世界数据集上的实验结果证明了该方法在多视图学习中的有效性。实验还分析了优化算法的参数敏感性和收敛性。