嵌入深度双线性交互信息和选择性融合的多视图学习
Embedded Deep Bilinear Interactive Information and Selective Fusion for Multi-view Learning
摘要：
多视图分类作为多视图学习的一种具体应用，通过对多个视图的最优融合，对传统的分类方法进行了改进。虽然以往的研究已经证明了多视点学习的优越性，但在深入研究中，还可以通过全面嵌入更强大的跨视点交互信息和更可靠的多视点融合策略来进一步提高多视点学习的优越性。为了实现这一目标，我们提出了一个新的多视图学习框架，使多视图分类更好地针对上述两个方面。也就是说，我们无缝地嵌入各种视图内的信息，跨视图的多维双线性交互信息，和一个新的视图集成机制到一个统一的框架，通过优化决策。特别地，我们训练不同的深度神经网络来学习各种视图内表示，然后通过视图之间的双线性函数从不同的双线性相似性动态地学习多维双线性交互信息。在此基础上，通过灵活调整视图权值的参数，自适应地融合多个视图的表示，不仅避免了权重的繁琐求解，而且为多视图分类提供了一种新的方法来选择有利于决策的少数有区别的视图.在六个公开数据集上的实验证明了该方法的有效性。
英文摘要：
As a concrete application of multi-view learning, multi-view classification improves the traditional classification methods significantly by integrating various views optimally. Although most of the previous efforts have been demonstrated the superiority of multi-view learning, it can be further improved by comprehensively embedding more powerful cross-view interactive information and a more reliable multi-view fusion strategy in intensive studies. To fulfill this goal, we propose a novel multi-view learning framework to make the multi-view classification better aimed at the above-mentioned two aspects. That is, we seamlessly embed various intra-view information, cross-view multi-dimension bilinear interactive information, and a new view ensemble mechanism into a unified framework to make a decision via the optimization. In particular, we train different deep neural networks to learn various intra-view representations, and then dynamically learn multi-dimension bilinear interactive information from different bilinear similarities via the bilinear function between views. After that, we adaptively fuse the representations of multiple views by flexibly tuning the parameters of the view-weight, which not only avoids the trivial solution of weight but also provides a new way to select a few discriminative views that are beneficial to make a decision for the multi-view classification. Extensive experiments on six publicly available datasets demonstrate the effectiveness of the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
