Traditional graph-based multi-view learning methods usually assume that data are complete. Whereas several instances of some views may be missing, making the corresponding graphs incomplete and reducing the virtue of graph regularization. To mitigate the negative effect, a novel method, called incomplete multi-view learning via consensus graph completion (IMLCGC), is proposed in this paper, which completes the incomplete graphs based on the consensus among different views and then fuses the completed graphs into a common graph. Specifically, IMLCGC develops a learning framework for incomplete multi-view data, which contains three components, i.e., consensus low-dimensional representation, graph regularization, and consensus graph completion. Furthermore, a generalization error bound of the model is established based on Rademacher’s complexity. It shows the theory that learning with incomplete multi-view data is difficult. Experimental results on six well-known datasets indicate that IMLCGC significantly outperforms the state-of-the-art methods.

传统的基于图的多视图学习方法通​​常假设数据是完整的。而某些视图的几个实例可能会丢失，从而使相应的图不完整并降低图正则化的优点。为了减轻负面影响，本文提出了一种新的方法，称为通过共识图完成的不完全多视图学习（IMLCGC），该方法基于不同视图之间的共识完成不完全图，然后将完成的图融合成一个常用图。具体来说，IMLCGC 开发了一个不完全多视图数据的学习框架，它包含三个组件，即共识低维表示、图正则化和共识图补全。此外，基于Rademacher的复杂性建立了模型的泛化误差界。它表明了使用不完整的多视图数据进行学习是困难的理论。六个知名数据集的实验结果表明，IMLCGC 显着优于最先进的方法。