The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Sparse subspace clustering
Abstract
We propose a method based on sparse representation (SR) to cluster data drawn from multiple low-dimensional linear or affine subspaces embedded in a high-dimensional space. Our method is based on the fact that each point in a union of subspaces has a SR with respect to a dictionary formed by all other data points. In general, finding such a SR is NP hard. Our key contribution is to show that, under mild assumptions, the SR can be obtained `exactly' by using l1optimization. The segmentation of the data is obtained by applying spectral clustering to a similarity matrix built from this SR. Our method can handle noise, outliers as well as missing data. We apply our subspace clustering algorithm to the problem of segmenting multiple motions in video. Experiments on 167 video sequences show that our approach significantly outperforms state-of-the-art methods.
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Sparse Representation for Color Image Restoration
Mairal, J., Elad, M., & Sapiro, G. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.911828 · Cited by 1,716
Cited by
Bayesian Saliency via Low and Mid Level Cues
Xie, Y., Lu, H., & Yang, M.-H. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2216276 · Cited by 367
Transfer adaptation learning: A decade survey
Zhang, L., & Gao, X. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183326 · Cited by 314
Smooth Representation Clustering
Hu, H., Lin, Z., Feng, J., & Zhou, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.484 · Cited by 286
(2010) · Cited by 15
Metadata
- Authors
- Elhamifar, E., & Vidal, R.
- Venue
- 2009 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 2790-2797
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2003217181
- Cited by
- 1,243
- References
- 3
- FWCI
- 31.73
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Elhamifar, E., & Vidal, R. (2009). Sparse subspace clustering. 2009 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2009.5206547
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