In multi-view and multi-label learning, each example can be represented by multiple data view features and annotated with a set of discrete non-exclusive labels. Missing label learning is an important branch of multi-label learning, which can handle incomplete labels with annotations. Previous work on multi-label learning with missing labels mainly considered data in a single view representation. Based on intuitive understanding, we propose a Two-step Multi-view and Multi-label Missing Label learning optimization solution(TM3L). The first step is to solve the multi-view learning problem by finding the data representation of the common low-dimensional space of all views through subspace learning. While fully considering the complementary information between multiple views, the different degrees of contribution combined with different views are weighted differently. The second step is to solve the multi-label missing label learning problem by using the label matrix completion method in combination with the kernel extreme learning machine classifier. The kernel extreme learning machine can effectively enhance the robustness of the algorithm to missing labels. The experimental results and analysis on multiple benchmark multi-view and multi-label data sets verify the effectiveness of TM3L compared with the state-of-the-art solutions.

在多视图和多标签学习中，每个示例都可以由多个数据视图功能表示，并用一组离散的非排他标签进行注释。缺少标签学习是多标签学习的重要分支，可以处理带有注释的不完整标签。先前关于缺少标签的多标签学习的工作主要是考虑单个视图表示中的数据。基于直观的认识，我们提出了一个牛逼WO步中号ULTI-视图和中号ULTI-标签中号伊辛大号abel学习优化解决方案（TM3L）。第一步是通过子空间学习找到所有视图的公共低维空间的数据表示，从而解决多视图学习问题。在充分考虑多个视图之间的补充信息时，将不同程度的贡献与不同视图结合在一起的权重也有所不同。第二步是通过使用标签矩阵完成方法结合内核极限学习机分类器来解决多标签缺失标签学习问题。内核极限学习机可以有效地增强算法对丢失标签的鲁棒性。与最新的解决方案相比，对多个基准多视图和多标签数据集的实验结果和分析证明了TM3L的有效性。