Subspace Clustering via Learning an Adaptive Low-Rank Graph

By using a sparse representation or low-rank representation of data, the graph-based subspace clustering has recently attracted considerable attention in computer vision, given its capability and efficiency in clustering data. However, the graph weights built using the representation coefficients ar...

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Veröffentlicht in:	IEEE transactions on image processing 2018-08, Vol.27 (8), p.3716-3728
Hauptverfasser:	Yin, Ming, Xie, Shengli, Wu, Zongze, Zhang, Yun, Gao, Junbin
Format:	Artikel
Sprache:	eng
Schlagworte:	adaptive low-rank graph affinity matrix Clustering algorithms Clustering methods Data models Image reconstruction low-rank representation Minimization Robustness Sparse matrices Sparse representation subspace clustering
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