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The low-rank matrix approximation problem with respect to the component-wise $\ell_1$-norm ($\ell_1$-LRA), which is closely related to robust principal component analysis (PCA), has become a very popular tool in data mining and machine learning.
In: Colloquium Mathematicae, vol. 23, pp. 165–171. Institute of Mathematics Polish Academy of Sciences (1971)
Brown, T., Spencer, J.: Minimization of · 1971
Earlier work this paper cites.
Technometrics
Gabriel, K., Zamir, S.: Lower rank approximation of matrices by least squares with any choice of weights · 1979
Earlier work this paper cites.
Mathematics of Control, Signals and Systems
Poljak, S., Rohn, J.: Checking robust nonsingularity is NP-hard · 1993
Earlier work this paper cites.
IEEE Trans. on Pattern Analysis and Machine Intelligence
Shum, H., Ikeuchi, K., Reddy, R.: Principal component analysis with missing data and its application to polyhedral object modeling · 1995
Earlier work this paper cites.
The Johns Hopkins University Press Baltimore (1996)
Golub, G., Van Loan, C.: Matrix Computation, 3rd Edition · 1996
Earlier work this paper cites.
Combinatorica
Frieze, A., Kannan, R.: Quick approximation to matrices and applications · 1999
Earlier work this paper cites.
Http://www.cs.yale.edu/homes/kannan/Papers/webgraph.pdf
Kannan, R., Vinay, V.: Analyzing the structure of large graphs (1999) · 1999
Earlier work this paper cites.
Linear and Multilinear Algebra
Rohn, J.: Computing the norm · 2000
Earlier work this paper cites.
Discrete Applied Mathematics
Asahiro, Y., Hassin, R., Iwama, K.: Complexity of finding dense subgraphs · 2002
Earlier work this paper cites.
http://www.cs.cmu.edu/afs/.cs.cmu.edu/Web/People/ke/publications/CMU-CS-03-172.pdf
Ke, Q., Kanade, T.: Robust subspace computation using L1 norm (2003) · 2003
Earlier work this paper cites.
In: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR ’05), pp. 739–746 (2005)
Ke, Q., Kanade, T.: Robust · 2005
Earlier work this paper cites.
SIAM J. on Computing
Alon, N., Naor, A.: Approximating the cut-norm via Grothendieck’s inequality · 2006
Earlier work this paper cites.
SIAM J. on Computing
Khot, S.: Ruling out PTAS for graph min-bisection, dense k-subgraph, and bipartite clique · 2006
Earlier work this paper cites.
IEEE Trans. on Pattern Analysis and Machine Intelligence
Kwak, N.: Principal component analysis based on L1-norm maximization · 2008
Earlier work this paper cites.
IEEE Transactions on Knowledge and Data Engineering
Miettinen, P., Mielikainen, T., Gionis, A., Das, G., Mannila, H.: The discrete basis problem · 2008
Cited alongside, same era.
In: S. Albers, A. Marchetti-Spaccamela, Y. Matias, S. Nikoletseas, W. Thomas (eds.) Automata, Languages and Programming,
Khuller, S., Saha, B.: On finding dense subgraphs · 2009
Cited alongside, same era.
IEEE Computer
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems · 2009
Cited alongside, same era.
In: Proc. of the 15th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD ’09, pp. 757–766 (2009)
Shen, B.H., Ji, S., Ye, J.: Mining discrete patterns via binary matrix factorization · 2009
Cited alongside, same era.
In: Advances in neural information processing systems (NIPS), pp. 2080–2088 (2009)
Wright, J., Ganesh, A., Rao, S., Peng, Y., Ma, Y.: Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization · 2009
Cited alongside, same era.
IEEE Trans. on Information Theory
Xu, H., Caramanis, C., Sanghavi, S.: Robust PCA via outlier pursuit · 2012
Later among the works it cites.
In: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR’ 12), pp. 1410–1417 (2012)
Zheng, Y., Liu, G., Sugimoto, S., Yan, S., Okutomi, M.: Practical low-rank matrix approximation under robust L1-norm · 2012
Later among the works it cites.
Applied Mathematics Letters
Brooks, J., Dulá, J.: The L1-norm best-fit hyperplane problem · 2013
Later among the works it cites.
Computational Statistics & Data Analysis
Brooks, J., Dulá, J., Boone, E.: A pure · 2013
Later among the works it cites.
SIAM J. on Optimization
Doan, X., Vavasis, S.: Finding approximately rank-one submatrices with the nuclear norm and · 2013
Later among the works it cites.
Discrete Mathematics
Fiorini, S., Kaibel, V., Pashkovich, K., Theis, D.: Combinatorial bounds on nonnegative rank and extended formulations · 2013
Later among the works it cites.
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In: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR ’10), pp. 771–778 (2010)
Eriksson, A., Van Den Hengel, A.: Efficient computation of robust low-rank matrix approximations in the presence of missing data using the · 2010
Cited alongside, same era.
Pattern Recognition
Gillis, N., Glineur, F.: Using underapproximations for sparse nonnegative matrix factorization · 2010
Cited alongside, same era.
Data Min. Knowl. Disc
Zhang, Z.Y., Li, T., Ding, C., Ren, X.W., Zhang, X.S.: Binary matrix factorization for analyzing gene expression data · 2010
Cited alongside, same era.
Mathematical programming
Ames, B., Vavasis, S.: Nuclear norm minimization for the planted clique and biclique problems · 2011
Cited alongside, same era.
Journal of the ACM
Candès, E., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? · 2011
Cited alongside, same era.
SIAM J. on Optimization
Chandrasekaran, V., Sanghavi, S., Parrilo, P., Willsky, A.: Rank-sparsity incoherence for matrix decomposition · 2011
Cited alongside, same era.
SIAM J. Matrix Anal. & Appl
Gillis, N., Glineur, F.: Low-rank matrix approximation with weights or missing data is NP-hard · 2011
Cited alongside, same era.
SIAM J. Matrix Anal. & Appl
Markovsky, I., Usevich, K.: Structured low-rank approximation with missing data · 2013
Later among the works it cites.
In: Advances in neural information processing systems (NIPS), pp. 2080–2088 (2014)
Netrapalli, P., Niranjan, U., Sanghavi, S., Anandkumar, A.: Non-convex robust PCA · 2014
Later among the works it cites.
IEEE Trans. on Information Theory
Qiu, C., Vaswani, N., Lois, B., Hogben, L.: Recursive robust PCA or recursive sparse recovery in large but structured noise · 2014
Later among the works it cites.
Automatica
Usevich, K., Markovsky, I.: Optimization on a Grassmann manifold with application to system identification · 2014
Later among the works it cites.
Foundations and Trends
Woodruff, D.: Sketching as a tool for numerical linear algebra · 2014
Later among the works it cites.
In: 56th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2015) (2015)
Clarkson, K., Woodruff, D.: Input sparsity and hardness for robust subspace approximation · 2015
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In: AAAI Conference on Artificial Intelligence, pp. 1198–1204 (2015)
Mirisaee, S., Gaussier, E., Termier, A.: Improved local search for binary matrix factorization · 2015
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In: 49th Annual ACM SIGACT Symposium on the Theory of Computing (STOC 2017) (2017)
Song, Z., Woodruff, D., Zhong, P.: Low rank approximation with entrywise · 2017
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