Support vector machine embedding discriminative dictionary pair learning for pattern classification

Discriminative dictionary learning (DDL) aims to address pattern classification problems via learning dictionaries from training samples. Dictionary pair learning (DPL) based DDL has shown superiority as compared with most existing algorithms which only learn synthesis dictionaries or analysis dictionaries. However, in the original DPL algorithm, the discrimination capability is only promoted via the reconstruction error and the structures of the learned dictionaries, while the discrimination of coding coefficients is not considered in the process of dictionary learning. To address this issue, we propose a new DDL algorithm by introducing an additional discriminative term associated with coding coefficients. Specifically, a support vector machine (SVM) based term is employed to enhance the discrimination of coding coefficients. In this model, a structured dictionary pair and SVM classifiers are jointly learned, and an optimization method is developed to address the formulated optimization problem. A classification scheme based on both the reconstruction error and SVMs is also proposed. Simulation results on several widely used databases demonstrate that the proposed method can achieve competitive performance as compared with some state-of-the-art DDL algorithms.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

Discriminative dictionary learning (DDL) is an approach to solving pattern classification problems by learning dictionaries from training samples. This study proposes a new DDL algorithm that enhances discrimination by introducing a discriminative term associated with coding coefficients. The algorithm employs a structured dictionary pair and support vector machines (SVMs) for joint learning, and a classification scheme based on reconstruction error and SVMs.