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

Authors :
Zhen-Hua Feng
Xiaojun Wu
Josef Kittler
He-Feng Yin
Source :
Pattern Analysis and Applications. 23:1381-1390
Publication Year :
2019
Publisher :
Springer Science and Business Media LLC, 2019.

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.<br />accepted by Pattern Analysis and Applications

Details

ISSN :
1433755X and 14337541
Volume :
23
Database :
OpenAIRE
Journal :
Pattern Analysis and Applications
Accession number :
edsair.doi.dedup.....fc70afb5ef4b01beac934ea92d40f8c0
Full Text :
https://doi.org/10.1007/s10044-019-00858-4