Multiple discriminant analysis for collaborative representation-based classification

doi:10.1016/j.patcog.2021.107819

Multiple discriminant analysis for collaborative representation-based classification

2021-04-01

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DOI：10.1016/j.patcog.2021.107819

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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. (c) 2021 Elsevier Ltd. All rights reserved.

Keywords:

Collaborative representation

Orthogonal discriminative projection

Face recognition

Binary classification

Multiple discriminant analysis for collaborative representation-based classification

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