Abstract
Face recognition is the important field in machine learning and pattern recognition research area. It has a lot of applications in military, finance, public security, to name a few. In this paper, the combination of the tensor sparse PCA with the nearest-neighbor method (and with the kernel ridge regression method) will be proposed and applied to the face dataset. Experimental results show that the combination of the tensor sparse PCA with any classification system does not always reach the best accuracy performance measures. However, the accuracy of the combination of the sparse PCA method and one specific classification system is always better than the accuracy of the combination of the PCA method and one specific classification system and is always better than the accuracy of the classification system itself.
Abstract (translated)
人脸识别是机器学习和模式识别研究的重要领域。它在军事、金融、公共安全等领域有很多应用。本文将张量稀疏PCA与最近邻法（和核岭回归法）相结合，并将其应用于人脸数据集。实验结果表明，张量稀疏主成分分析与任何分类系统的结合并不总能达到最佳的精度性能指标。然而，稀疏主成分分析方法与一个特定分类系统组合的准确度总是优于主成分分析方法与一个特定分类系统组合的准确度，并且总是优于分类系统本身的准确度。
URL
https://arxiv.org/abs/1904.08496
