Sparse Subspace Clustering: Algorithm, Theory, and Applications

By Ehsan Elhamifar, René Vidal. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013

651 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c80724f257eaca80af3abe9942e3f3003c81fa07

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. Lei Zhang
2. Wangmeng Zuo
3. David Dapeng Zhang
4. Ehsan Elhamifar
5. M. Soltanolkotabi
6. Dacheng Tao
7. René Vidal
8. Changqing Zhang

In Computer Vision and Pattern Recognition

1. Lei Zhang
2. Dacheng Tao
3. Luc Van Gool
4. Ming-Hsuan Yang
5. Wangmeng Zuo
6. Ling Shao
7. Xiaogang Wang
8. Shuicheng Yan

In Media Technology

1. Lei Zhang
2. Wangmeng Zuo
3. David Dapeng Zhang
4. Ehsan Elhamifar
5. Dacheng Tao
6. Xiaochun Cao
7. Changqing Zhang
8. Huan Fu

Citations in the corpus

• Latent Multi-view Subspace Clustering (2017, methodology): “Recently, researchers have extended the subspace clustering methods [7, 18] to latent representation based sub-space clustering.”
• On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization (2016, background): “Besides CCA, the recent proposed subspace clustering methods [54,22] resorted to explore the relationship between samples with self-representation (e.g., sparse subspace clustering (SSC) [20] and low-rank representation (LRR) [21]) in multi-view setting.”
• LSDT: Latent Sparse Domain Transfer Learning for Visual Adaptation (2016, methodology): “This is a typical sparse Lasso optimization problem with linear equality constraints, and can be efficiently solved by using alternative direction multiplier method (ADMM) in [3].”
• Going deeper into action recognition: A survey (2016, background): “3 Gaidon et al. (2014) SDT tree ATEP R 41.3 54.4 85.5 Simonyan and Zisserman (2014) Two-stream (CNN-M-2048) D 59.4 88.0 Transfer Learning on Sports 1M 65.4 Karpathy et al. (2014) Clip Hit @ 1 - Slow Fusion D 41.9 Video Hit @ 1 - Slow Fusion 60.9 Sun et al. (2015) Factorized Spatio Temporal Conv. Net”
• Diversity-induced Multi-view Subspace Clustering (2015, methodology): “Nevertheless, the other subspace clustering algorithms, such as SSC, LRR can also be implemented into our method.”
• Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering (2016, methodology): “In addition to these linear DR operators (e.g., a projection matrix), nonlinear DR techniques such as those used in spectral clustering (Ng et al., 2002) and sparse subspace clustering (Elhamifar & Vidal, 2013; You et al., 2016) have also been considered.”
• A Survey on Multi-View Clustering (2017, neutral): “que to find the underlying subspaces and then cluster the data points correctly according to the identified subspaces. 6 1) Subspace clustering: Subspace clustering uses the selfexpressiveness property [71] of the data samples, i.e., each sample canbe representedby a linear combinationof few other dat”
• Visualizing High-Dimensional Data: Advances in the Past Decade (2017, neutral): “The subspace clustering methods either find clusters in subset of the dimensions (originated from data mining [124]) or cluster points that share a low-dimensional linear subspace (originated from machine learning [36]).”
• Robust Subspace Clustering (2013, background): “Robust versions of SSC outperform the baseline algorithm.”