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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 6/2023

11.01.2023 | Original Article

Robust non-negative supervised low-rank discriminant embedding (NSLRDE) for feature extraction

verfasst von: Minghua Wan, Chengxu Yan, Tianming Zhan, Hai Tan, Guowei Yang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 6/2023

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Abstract

Among many feature extraction technologies, non-negative matrix factorization (NMF) technology ignores the global representation of data and focuses on the local structure information of data. However, the global representation is often more robust than data noise. Therefore, aiming at solving the above problems, combined with the characteristics of local information data, global representation and low-rank representation, a non-negative supervised low-rank discriminant embedding model (NSLRDE) is proposed to improve the robustness of the algorithm. The algorithm decomposes the data \(X\) into clean data \(A\) and noise data \(E\), and sparsely constrains \(E\) through \(L_{1}\)-norm to enhance the robustness to noise. In addition, the algorithm uses low-rank representation learning and non-negative decomposition to further enhance the robustness of the algorithm. Finally, combined with graph embedding algorithm, local and global data are retained. We also apply the method to various noise databases to test the effectiveness.
Vorheriger Artikel A novel framework based on the multi-label classification for dynamic selection of classifiers
Nächster Artikel Progressive image dehazing network based on dual feature extraction modules

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Metadaten
Titel
Robust non-negative supervised low-rank discriminant embedding (NSLRDE) for feature extraction
verfasst von
Minghua Wan
Chengxu Yan
Tianming Zhan
Hai Tan
Guowei Yang
Publikationsdatum
11.01.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 6/2023
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-022-01752-y

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