Learning a representation with the block-diagonal structure for pattern classification

He-Feng Yin, Xiao-Jun Wu, Josef Kittler, Zhen-Hua Feng

Abstract: Sparse-representation-based classification (SRC) has been widely studied and developed for various practical signal classification applications. However, the performance of a SRC-based method is degraded when both the training and test data are corrupted. To counteract this problem, we propose an approach that learns representation with block-diagonal structure (RBDS) for robust image recognition. To be more specific, we first introduce a regularization term that captures the block-diagonal structure of the target representation matrix of the training data. The resulting problem is then solved by an optimizer. Last, based on the learned representation, a simple yet effective linear classifier is used for the classification task. The experimental results obtained on several benchmarking datasets demonstrate the efficacy of the proposed RBDS method. The source code of our proposed RBDS is accessible at https://github.com/yinhefeng/RBDS.