Learning a Representation with the Block-Diagonal Structure for Pattern Classification
This is an incremental improvement for robust image recognition in pattern classification applications.
The paper tackled the problem of performance degradation in sparse-representation-based classification when both training and test data are corrupted, proposing a method that learns a representation with block-diagonal structure for robust image recognition, with experimental results on benchmarking datasets demonstrating its efficacy.
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.