Dual Graph Regularized Latent Low-Rank Representation for Subspace Clustering
Low-rank representation (LRR) has received considerable attention in subspace segmentation due to its effectiveness in exploring low-dimensional subspace structures embedded in data. To preserve the intrinsic geometrical structure of data, a graph regularizer has been introduced into LRR framework f...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 24(2015), 12 vom: 26. Dez., Seite 4918-33
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| 1. Verfasser: |
Yin, Ming
(VerfasserIn) |
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| Weitere Verfasser: |
Gao, Junbin,
Lin, Zhouchen,
Shi, Qinfeng,
Guo, Yi |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2015
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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
Research Support, Non-U.S. Gov't |
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