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arXiv · 1911.10301

Learning a Representation with the Block-Diagonal Structure for Pattern Classification

He-Feng Yin·Xiao-Jun Wu·Josef Kittler·Zhen-Hua Feng
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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.

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He-Feng Yin, Xiao-Jun Wu, Josef Kittler, Zhen-Hua Feng. 2019-11-23. Learning a Representation with the Block-Diagonal Structure for Pattern Classification. https://arxiv.org/abs/1911.10301

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