Zhao Liang, Yang Tao, Zhang Jie, Chen Zhikui, Yang Yi, Wang Z Jane
IEEE Trans Neural Netw Learn Syst. 2021 Apr;32(4):1486-1496. doi: 10.1109/TNNLS.2020.2984810. Epub 2021 Apr 2.
Multi-view data can represent objects from different perspectives and thus provide complementary information for data analysis. A topic of great importance in multi-view learning is to locate a low-dimensional latent subspace, where common semantic features are shared by multiple data sets. However, most existing methods ignore uncorrelated items (i.e., view-specific features) and may cause semantic bias during the process of common feature learning. In this article, we propose a non-negative correlated and uncorrelated feature co-learning (CoUFC) method to address this concern. More specifically, view-specific (uncorrelated) features are identified for each view when learning the common (correlated) feature across views in the latent semantic subspace. By eliminating the effects of uncorrelated information, useful inter-view feature correlations can be captured. We design a new objective function in CoUFC and derive an optimization approach to solve the objective with the analysis on its convergence. Experiments on real-world sensor, image, and text data sets demonstrate that the proposed method outperforms the state-of-the-art multiview learning methods.
多视图数据可以从不同视角表示对象，从而为数据分析提供补充信息。多视图学习中一个非常重要的课题是定位一个低维潜在子空间，多个数据集在该子空间中共享共同的语义特征。然而，大多数现有方法忽略了不相关的项（即特定视图的特征），并且在共同特征学习过程中可能会导致语义偏差。在本文中，我们提出了一种非负相关和不相关特征协同学习（CoUFC）方法来解决这一问题。更具体地说，在潜在语义子空间中跨视图学习共同（相关）特征时，为每个视图识别特定视图（不相关）的特征。通过消除不相关信息的影响，可以捕获有用的视图间特征相关性。我们在CoUFC中设计了一个新的目标函数，并通过对其收敛性的分析推导出一种优化方法来求解该目标。在真实世界的传感器、图像和文本数据集上的实验表明，所提出的方法优于现有的多视图学习方法。
