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Double competitive constraints-based collaborative representation for pattern classification
Computers & Electrical Engineering ( IF 4.3 ) Pub Date : 2020-06-01 , DOI: 10.1016/j.compeleceng.2020.106632
Jianping Gou , Hongwei Wu , Heping Song , Lan Du , Weihua Ou , Shaoning Zeng , Jia Ke

Abstract Representation-based classification (RBC) has attracted much attention in pattern recognition. As a linear representative RBC method, collaborative representation-based classification (CRC) is very promising for classification. Although many extensions of CRC have been developed recently, the discriminative and competitive representations of different classes for favorable classification has not yet been fully explored. To design the discriminative and competitive collaborative representations for enhancing the power of pattern discrimination, we propose a novel double competitive constraints-based collaborative representation for classification (DCCRC). In the proposed DCCRC, one competitive constraint is the l2-norm regularization of residuals between each query sample and the class-specific representations, the other one is the l2-norm regularization of the representations of all the classes excluding any one class. In two competitive constraints, the class discrimination information is employed to generate competitive representations. Moreover, the proposed method integrates both the representation learning and classification into the unified model. We study the effectiveness and robustness of the proposed method by comparing it with the state-of-the-art CRC methods on six face databases and twelve UCI data sets. The experimental results demonstrate the promising classification performance of the proposed method.

中文翻译:

基于双重竞争约束的模式分类协同表示

摘要 基于表征的分类(RBC)在模式识别领域备受关注。作为一种线性代表 RBC 方法,基于协同表示的分类(CRC)在分类方面非常有前途。尽管最近开发了 CRC 的许多扩展,但尚未充分探索不同类别的区分性和竞争性表示以进行有利分类。为了设计区分性和竞争性的协作表示以增强模式区分的能力,我们提出了一种新的基于双重竞争约束的分类协作表示(DCCRC)。在提议的 DCCRC 中,一个竞争约束是每个查询样本和特定于类的表示之间的残差的 l2 范数正则化,另一个是除任何一个类之外的所有类的表示的 l2 范数正则化。在两个竞争约束中,类别区分信息用于生成竞争表示。此外,所提出的方法将表示学习和分类都集成到统一模型中。我们通过在六个人脸数据库和十二个 UCI 数据集上将其与最先进的 CRC 方法进行比较来研究所提出方法的有效性和稳健性。实验结果证明了该方法具有良好的分类性能。所提出的方法将表示学习和分类都集成到统一模型中。我们通过在六个人脸数据库和十二个 UCI 数据集上将其与最先进的 CRC 方法进行比较来研究所提出方法的有效性和稳健性。实验结果证明了该方法具有良好的分类性能。所提出的方法将表示学习和分类都集成到统一模型中。我们通过在六个人脸数据库和十二个 UCI 数据集上将其与最先进的 CRC 方法进行比较来研究所提出方法的有效性和稳健性。实验结果证明了该方法具有良好的分类性能。
更新日期:2020-06-01
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