Low rank subspace clustering (LRSC)

By René Vidal, Paolo Favaro. Pattern Recognition Letters, 2014

486 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a5f4425bb9aebf5cce85559616ee321b416409d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Chong You
2. Daniel P. Robinson
3. Ivica Kopriva
4. Heng Huang
5. Cheng Deng
6. Mathieu Salzmann
7. Xu Yang
8. Zhiyuan Dang

In Computer Vision and Pattern Recognition

1. Cheng Deng
2. Dacheng Tao
3. Mathieu Salzmann
4. Luc Van Gool
5. Yi Yang
6. Xu Yang
7. Hongdong Li
8. Heng Huang

In Signal Processing

1. Ivica Kopriva
2. Maria Brbic
3. Cheng Deng
4. Heng Huang
5. Chong You
6. Daniel P. Robinson
7. Xu Yang
8. Zhiyuan Dang

Citations in the corpus

• Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit (2015, methodology): “These methods include SSC-BP [11, 12, 26], which uses the 1 norm as a regularizer, LRR [19] and LRSC [30], which use the nuclear norm, and LSR [22] which uses the 2 norm.”
• Deep Subspace Clustering Networks (2017, methodology): “We compare our methods against the following baselines: Low Rank Representation (LRR) [23], Low Rank Subspace Clustering (LRSC) [43], Sparse Subspace Clustering (SSC) [10], Kernel Sparse Subspace Clustering (KSSC) [35], SSC by Orthogonal Matching Pursuit (SSCOMP) [53], Efficient Dense Subspace Clust”
• Multi-Scale Fusion Subspace Clustering Using Similarity Constraint (2020, methodology): “The way to build affinity matrix could be roughly split into three categories: factorization based methods [9, 25], model based methods [3, 31], and self-expression based methods [4, 12, 19, 34].”
• Fast Robust PCA on Graphs (2015, unknown): “Fast Robust PCA on Graphs Nauman Shahid ∗ , Nathanael Perraudin, Vassilis Kalofolias, Gilles Puy † , Pierre Vandergheynst Email: { nauman.shahid, Signal Processing nathanael.perraudin, vassilis.kalofolias, pierre.vandergheynst @epfl.ch, † gilles.puy@inria.fr Switzerland Laboratory 2”
• Multi-view low-rank sparse subspace clustering (2017, methodology): “Sparse subspace clustering [15] and low-rank subspace clustering [16, 17, 18, 19] are among most effective methods that solve this problem.”
• A Weakly Supervised Fine Label Classifier Enhanced by Coarse Supervision (2019, methodology): “Low Rank and Sparse Representation (LRR, SR) methods are usually used in an unsupervised manner to capture low dimensional linear subspaces underlying the data [24, 9, 34, 7].”