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Margin embedding net for robust margin collaborative representation-based classification
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DOI：10.1016/j.patcog.2022.108991.png)
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Collaborative Representation-based Classification method (CRC) shows great potential in classification task. However, redundancies in both features and samples limit the application of CRC seriously. The existing works only solve one of them and ignore the other, which leads to performance degradation. To address this problem, we explore collaborative representation mechanism and propose a classification method termed Robust Margin Collaborative Representation-based Classification (RMCRC) which uses a few but more representative robust marginal samples to eliminate redundancy between samples. As the performance of RMCRC is related to robust marginal samples and class separability assumption closely, we further propose a feature extraction method termed Margin Embedding Net (MEN) for RMCRC. In MEN, virtual samples are generated by a generative model to enhance effectiveness of robust marginal samples and generalizability of RMCRC. Then, an embedding network with triplet loss is used to elimi-nate the redundancy in features and ensure the assumption is satisfied. Specifically, we construct triplet according to the collaborative representation. Hence, MEN fits RMCRC very well. Extensive experimental results validate effectiveness of proposed method.(c) 2022 Elsevier Ltd. All rights reserved.
Keyword:
Collaborative representation
Feature extraction
Marginal sample
Image classification
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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