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...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
IEEE
2017-01-01
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Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8080223/ |