Multiple discriminant analysis for collaborative representation-based classification

• Published in: Pattern recognition 2021-04, Vol.112, p.107819, Article 107819
• Main Authors: Zheng, Zhichao, Sun, Huaijiang, Zhou, Ying
• Format: Article
• Language: English
• Subjects: Binary classification; Collaborative representation; Face recognition; Orthogonal discriminative projection
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2021.107819

•A novel dimensionality reduction method for collaborative representation-based classification is proposed.•The discriminant analysis and binarization technique are used to extract more informative features.•A novel trace ratio algorithm is adopted to solve the proposed objective function.•The convergence is proved and the time complexity is analyzed.•Several experiments are conducted to show the effectiveness of the proposed method. Collaborative Representation-based Classifier (CRC) has shown its advantages and impressive results in face recognition. To further imporve the performance of CRC, we propose a novel dimensionality reduction method termed Multiple Discriminant Analysis for Collaborative Representation-based Classification (MDA-CRC). Considering the labeling criterion of CRC is class-specific, MDA-CRC solves a group of binary classification problems where specific feature subspaces are learned for each class. In each binary classification problem, an orthogonal discriminant analysis method based on collaborative representation is adopted. Hence, MDA-CRC can improve the discriminant ability of collaborative representation and be consistent with the labeling criterion of CRC simultaneously. Further, the convergence of MDA-CRC is proven. Extensive experiments on several benchmark datasets demonstrate the effectiveness of MDA-CRC.