Dornaika Fadi, Baradaaji Abdullah, Traboulsi Youssof El
IEEE Trans Neural Netw Learn Syst. 2022 Sep;33(9):4413-4423. doi: 10.1109/TNNLS.2021.3057270. Epub 2022 Aug 31.
Graph-based learning in semisupervised models provides an effective tool for modeling big data sets in high-dimensional spaces. It has been useful for propagating a small set of initial labels to a large set of unlabeled data. Thus, it meets the requirements of many emerging applications. However, in real-world applications, the scarcity of labeled data can negatively affect the performance of the semisupervised method. In this article, we present a new framework for semisupervised learning called joint label inference and discriminant embedding for soft label inference and linear feature extraction. The proposed criterion and its associated optimization algorithm take advantage of both labeled and unlabeled data samples in order to estimate the discriminant transformation. This type of criterion should allow learning more discriminant semisupervised models. Nine public image data sets are used in the experiments and method comparisons. These experimental results show that the performance of the proposed method is superior to that of many advanced semisupervised graph-based algorithms.
半监督模型中基于图的学习为在高维空间中对大数据集进行建模提供了一种有效工具。它对于将一小部分初始标签传播到大量未标记数据很有用。因此，它满足了许多新兴应用的需求。然而，在实际应用中，标记数据的稀缺会对半监督方法的性能产生负面影响。在本文中，我们提出了一种新的半监督学习框架，称为联合标签推理和判别嵌入，用于软标签推理和线性特征提取。所提出的准则及其相关的优化算法利用标记和未标记的数据样本，以估计判别变换。这种类型的准则应该允许学习更具判别力的半监督模型。实验和方法比较中使用了九个公共图像数据集。这些实验结果表明，所提出方法的性能优于许多先进的基于图的半监督算法。
