  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.

Computer Vision and Pattern Recognition