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

He-Feng, Yin, Xiao-Jun, Wu, Kittler, Josef and Zhen-Hua, Feng (2019) Learning a representation with the block-diagonal structure for pattern classification. Pattern Analysis and Applications, 210.

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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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
He-Feng, Yin
Xiao-Jun, Wu
Kittler, JosefJ.Kittler@surrey.ac.uk
Zhen-Hua, Feng
Date : 12 December 2019
Funders : Engineering and Physical Sciences Research Council (EPSRC), Ministry of Defence
DOI : 10.1007/s10044-019-00858-4
Grant Title : FACER2VM
Copyright Disclaimer : This is a post-peer-review, pre-copyedit version of an article published in Pattern Analysis and Applications. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10044-019-00858-4
Uncontrolled Keywords : Pattern classifcation; Low-rank and sparse representation; Block-diagonal structure
Depositing User : James Marshall
Date Deposited : 23 Jan 2020 11:49
Last Modified : 24 Apr 2020 14:36
URI: http://epubs.surrey.ac.uk/id/eprint/853369

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