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...

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Veröffentlicht in:	IEEE transactions on image processing 2015-12, Vol.24 (12), p.4918-4933
Hauptverfasser:	Yin, Ming, Gao, Junbin, Lin, Zhouchen, Shi, Qinfeng, Guo, Yi
Format:	Artikel
Sprache:	eng
Schlagworte:	Australia Convergence Data models Dual graph regularization Graph Laplacian Image clustering Laplace equations Low-rank representation Manifold structure Manifolds Noise Optimization
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