Sparse subspace clustering

By Ehsan Elhamifar, René Vidal. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009

1,495 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5facbc5bd594f4faa0524d871d49ba6a6e956e17

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In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Sparse Subspace Clustering: Algorithm, Theory, and Applications (2013, methodology): “Using advances in sparse [29], [30], [31] and low-rank [32], [33], [34] recovery algorithms, the Sparse Subspace Clustering (SSC) [35], [36], [37], Low-Rank Recovery (LRR) [38], [39], [40], and Low-Rank Subspace Clustering (LRSC) [41] algorithms pose the clustering problem as one of finding a sparse”
Video Segmentation by Non-Local Consensus voting (2014, methodology): “Later, trajectory-based methods have been proposed, some factorization-based [8, 13, 29], others based on spectral-clustering [4, 14, 25, 26, 31].”
A Geometric Analysis of Subspace Clustering with Outliers (2011, methodology): “In [11], Elhamifar and Vidal show that the subspace detection property holds as long as the subspaces are independent.”
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning (2018, background): “(2009), subspace clustering Elhamifar and Vidal (2009), few-shot learning Lee et al.”
Robust Subspace Clustering (2013, methodology): “Robust versions of SSC outperform the baseline algorithm.”
Deep Subspace Clustering Networks (2017, methodology): “, image segmentation [50, 27], motion segmentation [17, 9], and image clustering [14, 10].”
Action recognition in videos acquired by a moving camera using motion decomposition of Lagrangian particle trajectories (2011, background): “Since the camera motion subspace is approximately spanned by three basis [21, 25], the camera motion component can be estimated by Ac = US V ′ , where U and V are obtained by singular value decomposition [U, S, V ] = SV D(A), and S is equal to S except that all the singular values other than the mos”
Object Segmentation by Long Term Analysis of Point Trajectories (2010, methodology): “Recent works suggest ways to deal with these problems [19,20], but as the problems are inherent to factorization, this can only be successful up to a certain degree.”
Robust Subspace Segmentation by Low-Rank Representation (2010, methodology): “As the data dimension must be bounded above by 12 (Elhamifar & Vidal, 2009), we use a preprocessing step to project the data to be 12- dimensional by PCA.”
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence (2019, neutral): “Subspace [9] 46 7 14 4 MPPCA [20] 57 6 19 3 DSC [55] 60 5 19 2 Sparse dict.”
See all by looking at a few: Sparse modeling for finding representative objects (2012, methodology): “The optimization program (14) can also address the connectivity issues [32] of subspace clustering algorithms based on sparse representation [11, 15, 35] or low-rank representation [18, 29].”
Video Pop-up: Monocular 3D Reconstruction of Dynamic Scenes (2014, methodology): “Motion segmentation has also been cast as a motion subspace clustering problem, first applied to the affine camera case [9], [19] and later extended to the case of perspective scenes [22].”
Classification and clustering via dictionary learning with structured incoherence and shared features (2010, methodology): “The hypothesis from [8] that the subspaces span independent subspaces is very strong.”
A Weakly Supervised Fine Label Classifier Enhanced by Coarse Supervision (2019, methodology): “These subspaces usually have a selfexpressive property, meaning that a sample taken from a single subspace can be expressed as a linear combination of other samples from the same subspace [7, 27].”
A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture (2018, background): “This layer aims at encoding the self-expressiveness property [39] [40] of data drawn from a union of subspaces.”
Electronic Nose and Its Applications: A Survey (2019, neutral): “Group sparsity[287–290] and its specific varieties, such as block sparsity[291, 292], are examples of addition-”
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit (2015, methodology): “In particular, our result for deterministic arbitrary models is much stronger than the main result in [40].”
Robust Lasso With Missing and Grossly Corrupted Observations (2011, background): “Feature selection refers to the property that the recovered parameter has the same signed support as the true regressor.”