Incomplete Multi-view Learning via Consensus Graph Completion
Incomplete Multi-view Learning via Consensus Graph Completion
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DOI：
10.1007/s11063-022-10973-9
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发表时间：
2022-08
影响因子：
3.1
通讯作者：
Heng Zhang;Xiaohong Chen;Enhao Zhang-;Liping Wang
中科院分区：
文献类型：
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作者：
Heng Zhang;Xiaohong Chen;Enhao Zhang-;Liping Wang
传统的基于图的多视图学习方法通常假设数据是完整的。然而，某些视图的几个实例可能会丢失，使相应的图不完整，并降低了图正则化的优点。为了减轻这种负面影响，本文提出了一种新的基于一致性图完成的不完全多视图学习方法（IMLCGC），该方法根据不同视图之间的一致性完成不完全图，然后将完成的图融合成一个公共图。具体来说，IMLCGC开发了一个用于不完整多视图数据的学习框架，该框架包含三个组件，即，共识低维表示、图正则化和共识图完成。基于Rademacher的复杂性，建立了模型的推广误差界.它表明了不完整的多视图数据的学习是困难的理论。在六个知名数据集上的实验结果表明，IMLCGC显著优于现有的方法。
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.