Smooth Representation Clustering

By Han Hu, Zhouchen Lin, Jianjiang Feng, Jie Zhou. 2014

275 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

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

In Artificial Intelligence

1. Dacheng Tao
2. Yun Raymond Fu
3. Jiashi Feng
4. Tao A. Xiang
5. Huan Fu
6. Changqing Zhang
7. Yonghong Tian
8. Yaowei Wang

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Luc Van Gool
3. Yi Yang
4. Yun Raymond Fu
5. Jiashi Feng
6. Ming-Hsuan Yang
7. Ling Shao
8. Xiaogang Wang

In Media Technology

1. Yuan Xie
2. Yonghong Tian
3. Yaowei Wang
4. Mengyue Geng
5. Joey Tianyi Zhou
6. Dacheng Tao
7. Xi Peng
8. Qinghua Hu

Citations in the corpus

• Subspace Clustering by Block Diagonal Representation (2018, unknown): “CASS [24] SMR [14] BD-SSC [11] BD-LRR [11] BDR-Z 1.”
• Multi-View Clustering via Deep Matrix Factorization (2017, methodology): “(8) NaMSC (Cao et al. 2015) firstly applies (Hu et al. 2014) to each view data, then combines the learned representations and feeds to the spectral clustering.”
• Structured AutoEncoders for Subspace Clustering (2018, background): “1) Baseline Algorithms: We compare the proposed StructAEs with SSC [7], LRR [8], low rank based subspace clustering (LRSC) [41], LSR [6], smooth representation clustering (SMR) [42], kernel SSC (KSSC) [24], kernel LRR (KLRR) [23], latent subspace sparse subspace clustering (LS3C) [30] and stacked sp”
• Deep Transfer Learning for Person Re-Identification (2016, methodology): “To this end, we choose a graph regularised subspace learning model [25], [26].”
• Latent Multi-view Subspace Clustering (2017, methodology): “SMooth Representation clustering (SMR) [12] analyzes the grouping effect of self-representation based methods in depth.”
• Generalized Latent Multi-View Subspace Clustering (2020, background): “Constrained by graph regularization, SMooth Representation clustering (SMR) [11] investigates theoretically the grouping effect for self-representation based approaches.”
• Diversity-induced Multi-view Subspace Clustering (2015, methodology): “Our method is based on SMR because SMR is the state-of-the-art method.”
• Low-Rank Tensor Constrained Multiview Subspace Clustering (2015, methodology): “To cluster the data into their respective subspaces, we firstly compute a subspace representation matrix Z following [15, 28, 21]:”