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Subspace clustering refers to the task of finding a multi-subspace representation that best fits a collection of points taken from a high-dimensional space.
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Zhang, T.T., Szlam, A.A. andLerman, G.G. (2009). Median k k -flats for hybrid linear modeling with many outliers. In IEEE International Conference on Computer Vision Workshops, ICCV 234–241
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Zhang, A.A., Fawaz, N.N., Ioannidis, S.S. andMontanari, A.A. (2012). Guess who rated this movie: Identifying users through subspace clustering. In Proceedings of the International Conference on Uncertainty in Articial Intelligence 944–953
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Zhang, TengT., Szlam, ArthurA., Wang, YiY. andLerman, GiladG. (2012). Hybrid linear modeling via local best-fit flats. Int. J. Comput. Vis. 100 217–240
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Elhamifar, EhsanE. andVidal, RenéR. (2013). Sparse subspace clustering: Algorithms, theory, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 35 2765–2781
2013
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Liu, G.G., Lin, Z.Z., Yan, S.S., Sun, J.J., Yu, Y.Y. andMa, Y.Y. (2013). Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35 171–184
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Rosenbaum, M.M. andTsybakov, A. B.A. B. (2013). Improved matrix uncertainty selector. In From Probability to Statistics and Back: High-Dimensional Models and Processes—A Festschrift in Honor of Jon A. Wellner 276–290. IMS, Beachwood, OH
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McWilliams, BrianB. andMontana, GiovanniG. (2014). Subspace clustering of high-dimensional data: A predictive approach. Data Min. Knowl. Discov. 28 736–772
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Soltanolkotabi, M.M., Elhamifar, E.E. andCandès, E. J.E. J. (2014). Supplement to “Robust subspace clustering.” DOI: \doiurl
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Bayati, MohsenM. andMontanari, AndreaA. (2012). The LASSO risk for Gaussian matrices. IEEE Trans. Inform. Theory 58 1997–2017
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