Efficient Solvers for Sparse Subspace Clustering

Pourkamali-Anaraki, 2017, Preconditioned data sparsification for big data with applications to PCA and K-means, IEEE Transactions on Information Theory, 63, 2954 Bachem, 2018, Scalable K-means clustering via lightweight coresets Soltanolkotabi, 2012, A geometric analysis of subspace clustering with outliers, The Annals of Statistics, 40, 2195, 10.1214/12-AOS1034 Vidal, 2016, Sparse and low-rank methods Elhamifar, 2013, Sparse subspace clustering: Algorithm, theory, and applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 2765, 10.1109/TPAMI.2013.57 Heckel, 2017, Dimensionality-reduced subspace clustering, Information and Inference: A Journal of the IMA, 6, 246, 10.1093/imaiai/iaw021 Elhamifar, 2009, Sparse subspace clustering Patel, 2013, Latent space sparse subspace clustering Li, 2018, On geometric analysis of affine sparse subspace clustering, IEEE Journal of Selected Topics in Signal Processing, 12, 1520, 10.1109/JSTSP.2018.2867446 Luxburg, 2007, A tutorial on spectral clustering, Statistics and computing, 17, 395, 10.1007/s11222-007-9033-z Schiebinger, 2015, The geometry of kernelized spectral clustering, The Annals of Statistics, 43, 819, 10.1214/14-AOS1283 Arias-Castro, 2019, Unconstrained and curvature-constrained shortest-path distances and their approximation, Discrete & Computational Geometry, 62, 1, 10.1007/s00454-019-00060-7 Tremblay, 2020, Approximating spectral clustering via sampling: a review Soltanolkotabi, 2014, Robust subspace clustering, The Annals of Statistics, 42, 669, 10.1214/13-AOS1199 You, 2015, Geometric conditions for subspace-sparse recovery Tsakiris, 2018, Theoretical analysis of sparse subspace clustering with missing entries Boyd, 2011, Distributed optimization and statistical learning via the alternating direction method of multipliers, Foundations and Trends in Machine learning, 3, 1, 10.1561/2200000016 Xu, 2017, ADMM without a fixed penalty parameter: Faster convergence with new adaptive penalization Adler, 2015, Linear-time subspace clustering via bipartite graph modeling, IEEE Transactions on Neural Networks and Learning Systems, 26, 2234, 10.1109/TNNLS.2014.2374631 Traganitis, 2017, Sketched subspace clustering, IEEE Transactions on Signal Processing, 66, 1663, 10.1109/TSP.2017.2781649 You, 2018, Scalable exemplar-based subspace clustering on class-imbalanced data Abdolali, 2019, Scalable and robust sparse subspace clustering using randomized clustering and multilayer graphs, Signal Processing, 163, 166, 10.1016/j.sigpro.2019.05.017 Pourkamali-Anaraki, 2019, Large-scale sparse subspace clustering using landmarks Dyer, 2013, Greedy feature selection for subspace clustering, Journal of Machine Learning Research, 14, 2487 Chen, 2018, Active orthogonal matching pursuit for sparse subspace clustering, IEEE Signal Processing Letters, 25, 164, 10.1109/LSP.2017.2741509 C. You, D. Robinson, R. Vidal, 2016a. pages->3918–3927, unknown->Scalable sparse subspace clustering by orthogonal matching pursuit, in: IEEE Conference on Computer Vision and Pattern Recognition, pp. You, 2016, Oracle based active set algorithm for scalable elastic net subspace clustering You, 2019, Is an affine constraint needed for affine subspace clustering? Jiang, 2018, A nonconvex formulation for low rank subspace clustering: algorithms and convergence analysis, Computational Optimization and Applications, 70, 395, 10.1007/s10589-018-0002-6 Yang, 2016, ℓ0-sparse subspace clustering Xu, 2017, Adaptive ADMM with spectral penalty parameter selection Combettes, 2005, Signal recovery by proximal forward-backward splitting, SIAM Multiscale Model. Simul., 4, 1168, 10.1137/050626090 Beck, 2017, First-order methods in optimization Attouch, 2011, Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward–backward splitting, and regularized Gauss-Seidel methods, Mathematical Programming, 1 Nesterov, 2004, Introductory lectures on convex optimization: A basic course, 10.1007/978-1-4419-8853-9 Bauschke, 2017, Convex analysis and monotone operator theory in hilbert spaces Dussault, 1986, Convex quadratic programming with one constraint and bounded variables, Mathematical Programming, 36, 90, 10.1007/BF02591992 Stefanov, 2004, Polynomial algorithms for projecting a point onto a region defined by a linear constraint and box constraints in Rn, Journal of Applied Mathematics, 2004, 409, 10.1155/S1110757X04309071 Kiwiel, 2007, On linear-time algorithms for the continuous quadratic knapsack problem, Journal of Optimization Theory and Applications, 134, 549, 10.1007/s10957-007-9259-0 Bayón, 2010, An analytic solution for some separable convex quadratic programming problems with equality and inequality constraints, Journal of Mathematical inequalities, 4, 453, 10.7153/jmi-04-42 Brucker, 1984, An O(n) algorithm for quadratic knapsack problems, Operations Research Letters, 3, 163, 10.1016/0167-6377(84)90010-5 Liu, 2009, Efficient Euclidean projections in linear time Duchi, 2008, Efficient projections onto the l1-ball for learning in high dimensions, International Conference on Machine Learning E.V.d. Berg, M. Schmidt, M.P. Friedlander, K. Murphy, Group sparsity via linear time projection, 2008, Tech. Rep. TR-2008-09, Dept. Comp. Sci., U. British Columbia (June). Pardalos, 1990, An algorithm for a singly constrained class of quadratic programs subject to upper and lower bounds, Mathematical Programming, 46, 321, 10.1007/BF01585748 Maculan, 1989, A linear-time median-finding algorithm for projecting a vector on the simplex of Rn, Operations research letters, 8, 219, 10.1016/0167-6377(89)90064-3 Becker, 2012, A quasi-Newton proximal splitting method Knuth, 1997, The art of computer programming, Vol. 3, Pearson Education Becker, 2013, Sparse projections onto the simplex S. Becker, E.J. Candés, M. Grant, Templates for convex cone problems with applications to sparse signal recovery, Mathematical programming computation 3(3). Georghiades, 2001, From few to many: Illumination cone models for face recognition under variable lighting and pose, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 643, 10.1109/34.927464 Heckel, 2015, Robust subspace clustering via thresholding, IEEE Transactions on Information Theory, 61, 6320, 10.1109/TIT.2015.2472520