Learning Latent Low-Rank and Sparse Embedding for Robust Image Feature Extraction
To defy the curse of dimensionality, the inputs are always projected from the original high-dimensional space into the target low-dimension space for feature extraction. However, due to the existence of noise and outliers, the feature extraction task for corrupted data is still a challenging problem...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 29(2020), 1 vom: 06., Seite 2094-2107
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| 1. Verfasser: |
Ren, Zhenwen
(VerfasserIn) |
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| Weitere Verfasser: |
Sun, Quansen,
Wu, Bin,
Zhang, Xiaoqian,
Yan, Wenzhu |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2020
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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| Schlagworte: | Journal Article |
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