School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
School of Software, Xi'an Jiaotong University, Xi'an, 710049, China.
Neural Netw. 2021 Oct;142:92-104. doi: 10.1016/j.neunet.2021.04.033. Epub 2021 May 5.
At present, the diversity of data acquisition boosts the growth of multi-view data and the lack of label information. Since manually labeling is expensive and impractical, it is practical to enhance learning performance with a small amount of labeled data and a large amount of unlabeled data. In this study, we propose a novel multi-view semi-supervised learning (MSEL) framework termed flexible MSEL (FMSEL) with unified graph. In this framework, two flexible regression residual terms are introduced. One is a linear penalty term, which adaptively weighs the diverse contributions of different views and properly learns a well structured unified graph. The other is a relaxation regularization term, which finds the optimal prediction and the linear regression function. Both the prediction of samples in the database and new-coming data are supported. Moreover, during the process, the unified graph learns depending on the data structure and dynamically updated label information. Further, we provide an alternating optimization algorithm to iteratively solve the resultant objective problem and theoretically analyze the corresponding complexities. Extensive experiments conducted on synthetic and public datasets demonstrate the superiority of FMSEL.
目前，数据采集的多样性促进了多视图数据的增长和标签信息的缺乏。由于手动标记既昂贵又不切实际，因此使用少量标记数据和大量未标记数据来提高学习性能是切实可行的。在本研究中，我们提出了一种新颖的多视图半监督学习（MSEL）框架，称为灵活的 MSEL（FMSEL），具有统一的图。在这个框架中，引入了两个灵活的回归残差项。一个是线性惩罚项，它自适应地权衡不同视图的不同贡献，并适当学习一个结构良好的统一图。另一个是松弛正则化项，它寻找最优预测和线性回归函数。这两种预测都支持数据库中的样本和新到来的数据。此外，在这个过程中，统一的图会根据数据结构和动态更新的标签信息进行学习。此外，我们还提供了一种交替优化算法来迭代求解所得的目标问题，并对相应的复杂度进行了理论分析。在合成和公共数据集上进行的广泛实验表明了 FMSEL 的优越性。
