Orthogonal Multi-view Analysis by Successive Approximations via Eigenvectors
Orthogonal Multi-view Analysis by Successive Approximations via Eigenvectors
复制标题
DOI：
10.1016/j.neucom.2022.09.018
复制
发表时间：
2020-10
期刊：
影响因子：
6
通讯作者：
L. xilinx Wang;Lei-Hong Zhang;Chungen Shen;Ren-Cang Li
中科院分区：
文献类型：
--
作者：
L. xilinx Wang;Lei-Hong Zhang;Chungen Shen;Ren-Cang Li
正交性已被证明具有许多理想的性质，如容忍噪声、有利于数据可视化和保持距离。然而，它通常与现有模型不兼容，所产生的优化问题即使是兼容的也是具有挑战性的。为了解决这些问题，我们提出了一种用于多视点空间学习的迹比公式，以学习所有视点的单个正交投影。该方法将多视点的相关性、有监督的判别能力和距离保持简明紧凑地结合在一起。它不仅包括了几个现有的模型作为特例，而且还启发了新的模型。此外，还提出了一种基于特征向量逐次逼近的有效数值方法来求解相关优化问题。该方法建立在迭代Krylov子空间方法的基础上，该方法可以很容易地扩展到高维数据集。在各种真实数据集上进行了广泛的实验，以进行多视点判别分析和多视点多标签分类。实验结果表明，所提出的模型始终与比较的方法相竞争，而且往往优于比较的方法。
Orthogonality has been demonstrated to admit many desirable properties such as noise-tolerant, good for data visualization, and preserving distances. However, it is often incompatible with existing models and the resulting optimization problem is challenging even if compatible. To address these issues, we propose a trace ratio formulation for multi-view subspace learning to learn individual orthogonal projections for all views. The proposed formulation integrates the correlations within multiple views, supervised discriminant capacity, and distance preservation in a concise and compact way. It not only includes several existing models as special cases, but also inspires new models. Moreover, an efficient numerical method based on successive approximations via eigenvectors is presented to solve the associated optimization problem. The method is built upon an iterative Krylov subspace method which can easily scale up for high-dimensional datasets. Extensive experiments are conducted on various real-world datasets for multi-view discriminant analysis and multi-view multi-label classification. The experimental results demonstrate that the proposed models are consistently competitive to and often better than the compared methods.