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Pattern Analysis and Applications

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12-12-2019 | Short paper

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

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

Published in: Pattern Analysis and Applications | Issue 3/2020

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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. The source code of our proposed RBDS is accessible at https://github.com/yinhefeng/RBDS.

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Title: Learning a representation with the block-diagonal structure for pattern classification
Authors: He-Feng Yin
Xiao-Jun Wu
Josef Kittler
Zhen-Hua Feng
Publication date: 12-12-2019
Publisher: Springer London
Published in: Pattern Analysis and Applications / Issue 3/2020
Print ISSN: 1433-7541
Electronic ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-019-00858-4

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