School of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Tech. for Industrial Cyberspace, Jiangsu University, Zhenjiang, 212013, China.
College of Electrical and Information Engineering, North Minzu University, Yinchuan, 750021, Ningxia, China.
Neural Netw. 2022 Jun;150:12-27. doi: 10.1016/j.neunet.2022.02.021. Epub 2022 Mar 4.
Collaborative representation-based classification (CRC), as a typical kind of linear representation-based classification, has attracted more attention due to the effective and efficient pattern classification performance. However, the existing class-specific representations are not competitively learned from collaborative representation for achieving more informative pattern discrimination among all the classes. With the purpose of enhancing the power of competitive and discriminant representations among all the classes for favorable classification, we propose a novel CRC method called the class-specific mean vector-based weighted competitive and collaborative representation (CMWCCR). The CMWCCR mainly contains three discriminative constraints including the competitive, mean vector and weighted constraints that fully employ the discrimination information in different ways. In the competitive constraint, the representations from any one class and the other classes are adapted for learning competitive representations among all the classes. In the newly designed mean vector constraint, the mean vectors of all the class-specific training samples with the corresponding class-specific representations are taken into account to further enhance the competitive representations. In the devised weighted constraint, the class-specific weights are constrained on the representation coefficients to make the similar classes have more representation contributions to strengthening the discrimination among all the class-specific representations. Thus, these three constraints in the unified CMWCCR model can complement each other for competitively learning the discriminative class-specific representations. To verify the CMWCCR classification performance, the extensive experiments are conducted on twenty-eight data sets in comparisons with the state-of-the-art representation-based classification methods. The experimental results show that the proposed CMWCCR is an effective and robust CRC method with satisfactory performance.
协同表示分类（CRC）作为一种典型的线性表示分类方法，由于其有效的和高效的模式分类性能而受到了更多的关注。然而，现有的类特定表示并没有从协同表示中竞争学习，以实现所有类之间更具信息量的模式区分。为了提高所有类之间竞争和判别表示的能力，以实现有利的分类，我们提出了一种新的 CRC 方法，称为基于类特定均值向量的加权竞争和协同表示（CMWCCR）。CMWCCR 主要包含三个判别约束，包括竞争、均值向量和加权约束，它们以不同的方式充分利用了不同的判别信息。在竞争约束中，来自任何一个类和其他类的表示被用于学习所有类之间的竞争表示。在新设计的均值向量约束中，考虑了所有类特定训练样本及其相应类特定表示的均值向量，以进一步增强竞争表示。在设计的加权约束中，对表示系数进行类特定加权约束，使相似类对增强所有类特定表示之间的判别有更多的表示贡献。因此，这三个约束在统一的 CMWCCR 模型中可以相互补充，以竞争学习有判别力的类特定表示。为了验证 CMWCCR 的分类性能，我们在 28 个数据集上进行了广泛的实验，并与最先进的基于表示的分类方法进行了比较。实验结果表明，所提出的 CMWCCR 是一种有效且稳健的 CRC 方法，具有令人满意的性能。
