A Fast Generalized Low Rank Representation Framework Based on $L_{2,p}$ Norm Minimization for Subspace Clustering

A Fast Generalized Low Rank Representation Framework Based on $L_{2,p}$ Norm Minimization for Subspace Clustering

Low rank representation (LRR) is powerful for subspace clustering due to its strong ability in exploring low-dimensional subspace structures embedded in data. LRR is usually solved by iterative nuclear norm minimization, which involves singular value decomposition (SVD) at each iteration. However, t...

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Bibliographic Details
Main Authors: Tao Zhang, Zhenmin Tang, Xiaobo Shen
Format: Article
Language:English
Published: IEEE 2017-01-01
Series:IEEE Access
Subjects:
Subspace clustering
low rank representation
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Online Access:https://ieeexplore.ieee.org/document/8080223/

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https://ieeexplore.ieee.org/document/8080223/

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