论文标题
自我发作的多视图表示学习具有多样性的互补性
Self-attention Multi-view Representation Learning with Diversity-promoting Complementarity
论文作者
论文摘要
多视图学习尝试通过利用多视图数据之间的共识和/或互补性来生成具有更好性能的模型。但是，就互补性而言，大多数现有方法只能找到具有单一互补性的表示形式，而不是具有多样性的互补信息。在本文中，为了同时利用互补性和一致性，可以自由控制深度学习的潜力，以掌握多样性的多样性互补性，以进行多视图表示学习，我们提出了一种新颖的监督多视图表示算法，称为自我发挥多的多次视图网络，具有多样性的多样性complotity cromisters conformenting complomiting conformenting conforment fy Expering conforment fy samvdppc by samvdpc by samvdpc a conformention the Explosing a samvdpc by samvdpc a constemition（samvdpc a）自我关注以找到互补信息需要多样性。在八个现实世界数据集上进行的广泛实验证明了我们提出的方法的有效性，并显示了其优于几种基线方法的优势，该方法仅考虑单个互补信息。
Multi-view learning attempts to generate a model with a better performance by exploiting the consensus and/or complementarity among multi-view data. However, in terms of complementarity, most existing approaches only can find representations with single complementarity rather than complementary information with diversity. In this paper, to utilize both complementarity and consistency simultaneously, give free rein to the potential of deep learning in grasping diversity-promoting complementarity for multi-view representation learning, we propose a novel supervised multi-view representation learning algorithm, called Self-Attention Multi-View network with Diversity-Promoting Complementarity (SAMVDPC), which exploits the consistency by a group of encoders, uses self-attention to find complementary information entailing diversity. Extensive experiments conducted on eight real-world datasets have demonstrated the effectiveness of our proposed method, and show its superiority over several baseline methods, which only consider single complementary information.
