Zhang Chao, Li Huaxiong, Qian Yuhua, Chen Chunlin, Zhou Xianzhong
IEEE Trans Neural Netw Learn Syst. 2022 Mar;33(3):1254-1268. doi: 10.1109/TNNLS.2020.3041636. Epub 2022 Feb 28.
Regression-based methods have been widely applied in face identification, which attempts to approximately represent a query sample as a linear combination of all training samples. Recently, a matrix regression model based on nuclear norm has been proposed and shown strong robustness to structural noises. However, it may ignore two important issues: the label information and local relationship of data. In this article, a novel robust representation method called locality-constrained discriminative matrix regression (LDMR) is proposed, which takes label information and locality structure into account. Instead of focusing on the representation coefficients, LDMR directly imposes constraints on representation components by fully considering the label information, which has a closer connection to identification process. The locality structure characterized by subspace distances is used to learn class weights, and the correct class is forced to make more contribution to representation. Furthermore, the class weights are also incorporated into a competitive constraint on the representation components, which reduces the pairwise correlations between different classes and enhances the competitive relationships among all classes. An iterative optimization algorithm is presented to solve LDMR. Experiments on several benchmark data sets demonstrate that LDMR outperforms some state-of-the-art regression-based methods.
基于回归的方法已广泛应用于人脸识别，该方法试图将查询样本近似表示为所有训练样本的线性组合。最近，一种基于核范数的矩阵回归模型被提出，并显示出对结构噪声具有很强的鲁棒性。然而，它可能忽略了两个重要问题：数据的标签信息和局部关系。在本文中，提出了一种新的鲁棒表示方法，称为局部约束判别矩阵回归（LDMR），该方法考虑了标签信息和局部结构。LDMR不是关注表示系数，而是通过充分考虑标签信息直接对表示分量施加约束，这与识别过程有更紧密的联系。以子空间距离为特征的局部结构用于学习类权重，并迫使正确的类对表示做出更大贡献。此外，类权重还被纳入对表示分量的竞争约束中，这减少了不同类之间的成对相关性，并增强了所有类之间的竞争关系。提出了一种迭代优化算法来求解LDMR。在几个基准数据集上的实验表明，LDMR优于一些基于回归的最新方法。
