Using The Matrix Ridge Approximation to Speedup Determinantal Point Processes Sampling Algorithms
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Zhihua Zhang | Chao Zhang | Shusen Wang | Hui Qian | Zhihua Zhang | Chao Zhang | Shusen Wang | Hui Qian
[1] Arild Stubhaug. Acta Mathematica , 1886, Nature.
[2] E. Nyström. Über Die Praktische Auflösung von Integralgleichungen mit Anwendungen auf Randwertaufgaben , 1930 .
[3] Charles R. Johnson,et al. Topics in Matrix Analysis , 1991 .
[4] David J. Spiegelhalter,et al. Machine Learning, Neural and Statistical Classification , 2009 .
[5] Tobias Scheffer,et al. International Conference on Machine Learning (ICML-99) , 1999, Künstliche Intell..
[6] Christopher K. I. Williams,et al. Using the Nyström Method to Speed Up Kernel Machines , 2000, NIPS.
[7] Jitendra Malik,et al. Spectral grouping using the Nystrom method , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[8] Petros Drineas,et al. On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning , 2005, J. Mach. Learn. Res..
[9] Santosh S. Vempala,et al. Matrix approximation and projective clustering via volume sampling , 2006, SODA '06.
[10] Y. Peres,et al. Determinantal Processes and Independence , 2005, math/0503110.
[11] S. Muthukrishnan,et al. Relative-Error CUR Matrix Decompositions , 2007, SIAM J. Matrix Anal. Appl..
[12] Ivor W. Tsang,et al. Improved Nyström low-rank approximation and error analysis , 2008, ICML '08.
[13] James T. Kwok,et al. Clustered Nyström Method for Large Scale Manifold Learning and Dimension Reduction , 2010, IEEE Transactions on Neural Networks.
[14] Ben Taskar,et al. Structured Determinantal Point Processes , 2010, NIPS.
[15] Ameet Talwalkar,et al. On the Impact of Kernel Approximation on Learning Accuracy , 2010, AISTATS.
[16] Christos Boutsidis,et al. Near Optimal Column-Based Matrix Reconstruction , 2011, 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science.
[17] James T. Kwok,et al. Time and space efficient spectral clustering via column sampling , 2011, CVPR 2011.
[18] Ben Taskar,et al. Learning Determinantal Point Processes , 2011, UAI.
[19] Nathan Halko,et al. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions , 2009, SIAM Rev..
[20] Ben Taskar,et al. k-DPPs: Fixed-Size Determinantal Point Processes , 2011, ICML.
[21] Ben Taskar,et al. Determinantal Point Processes for Machine Learning , 2012, Found. Trends Mach. Learn..
[22] Ameet Talwalkar,et al. Sampling Methods for the Nyström Method , 2012, J. Mach. Learn. Res..
[23] Venkatesan Guruswami,et al. Optimal column-based low-rank matrix reconstruction , 2011, SODA.
[24] Rong Jin,et al. Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison , 2012, NIPS.
[25] J. M. Aldaz. Sharp bounds for the difference between the arithmetic and geometric means , 2012, 1203.4454.
[26] Ben Taskar,et al. Nystrom Approximation for Large-Scale Determinantal Processes , 2013, AISTATS.
[27] Ben Taskar,et al. Approximate Inference in Continuous Determinantal Processes , 2013, NIPS.
[28] Ameet Talwalkar,et al. Large-scale SVD and manifold learning , 2013, J. Mach. Learn. Res..
[29] Zhihua Zhang,et al. The matrix ridge approximation: algorithms and applications , 2013, Machine Learning.
[30] Zhihua Zhang,et al. Improving CUR matrix decomposition and the Nyström approximation via adaptive sampling , 2013, J. Mach. Learn. Res..
[31] Zhihua Zhang,et al. Efficient Algorithms and Error Analysis for the Modified Nystrom Method , 2014, AISTATS.
[32] Michael W. Mahoney,et al. Revisiting the Nystrom Method for Improved Large-scale Machine Learning , 2013, J. Mach. Learn. Res..