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We propose a novel class of kernels to alleviate the high computational cost of large-scale nonparametric learning with kernel methods.
On lines and planes of closest fit to systems of points in space
K. Pearson · 1901
Earlier work this paper cites.
Some methods for classification and analysis of multivariate observations
J. MacQueen · 1967
Earlier work this paper cites.
Multidimensional binary search trees used for associative searching
J. L. Bentley · 1975
Earlier work this paper cites.
Curve fitting and optimal design for prediction
A. O’Hagan and J. F. C. Kingman · 1978
Earlier work this paper cites.
Extensions of Lipschitz mappings into a Hilbert space
W. B. Johnson and J. Lindenstrauss · 1984
Earlier work this paper cites.
A refined particle method for astrophysical problems
J. J. Monaghan and J. C. Lattanzio · 1985
Earlier work this paper cites.
Interpolation of scattered data: Distance matrices and conditionally positive definite functions
C. A. Micchelli · 1986
Earlier work this paper cites.
Topics in Matrix Analysis
R. A. Horn and C. R. Johnson · 1994
Earlier work this paper cites.
Matrix Computations
G. H. Golub and C. F. Van Loan · 1996
Earlier work this paper cites.
Handbook of statistics , volume 13, chapter 9 Computer Experiments, pages 261–308
J. R. Koehler and A. B. Owen · 1996
Earlier work this paper cites.
Nonlinear component analysis as a kernel eigenvalue problem
B. Schölkopf, A. Smola, and K.-R. Müller · 1998
Earlier work this paper cites.
LAPACK Users’ Guide
E. Anderson, Z. Bai, C. Bischof, L. S. Blackford, J. Demmel, J. Dongarra, J. D. Croz, A. Greenbaum, S. Hammarling, A. McKenney, and D. Sorensen · 1999
Earlier work this paper cites.
A sparse matrix arithmetic based on ℋ \mathcal{H} -matrices. part I: Introduction to ℋ \mathcal{H} -matrices
W. Hackbusch · 1999
Earlier work this paper cites.
Interpolation of Spatial Data: Some Theory for Kriging
M. L. Stein · 1999
Earlier work this paper cites.
Using the Nyström method to speed up kernel machines
C. K. I. Williams and M. Seeger · 2000
Earlier work this paper cites.
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
B. Schölkopf and A. J. Smola · 2001
Earlier work this paper cites.
A generalized representer theorem
B. Schölkopf, R. Herbrich, and A. J. Smola · 2001
Earlier work this paper cites.
A matrix version of the fast multipole method
X. Sun and N. P. Pitsianis · 2001
Earlier work this paper cites.
An elementary proof of a theorem of Johnson and Lindenstrauss
S. Dasgupta and A. Gupta · 2002
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Iterative Methods for Sparse Linear Systems
Y. Saad · 2003
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Scattered Data Approximation
H. Wendland · 2004
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On the Nyström method for approximating a Gram matrix for improved kernel-based learning
P. Drineas and M. W. Mahoney · 2005
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Covariance tapering for interpolation of large spatial datasets
R. Furrer, M. G. Genton, and D. Nychka · 2006
Cited alongside, same era.
Gaussian Processes for Machine Learning
C. E. Rasmussen and C. K. I. Williams · 2006
Cited alongside, same era.
Nyström method vs random Fourier features: A theoretical and empirical comparison
T. Yang, Y. feng Li, M. Mahdavi, R. Jin, and Z.-H. Zhou · 2012
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Hilbert space embeddings of predictive state representations
B. Boots, A. Gretton, and G. J. Gordon · 2013
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On the use of discrete Laplace operator for preconditioning kernel matrices
J. Chen · 2013
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Kernel-based methods for hypothesis testing: A unified view
Z. Harchaoui, F. Bach, O. Cappe, and E. Moulines · 2013
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Uncertainty Quantification: Theory, Implementation, and Applications
R. C. Smith · 2013
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Hilbert space embeddings of hidden Markov models
L. Song, B. Boots, S. Siddiqi, G. Gordon, and A. Smola · 2013
Later among the works it cites.
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High-performance implementation of the level-3 BLAS
K. Goto and R. V. D. Geijn · 2008
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Covariance tapering for likelihood-based estimation in large spatial data sets
C. Kaufman, M. Schervish, and D. Nychka · 2008
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Improved Nyström low-rank approximation and error analysis
K. Zhang, I. W. Tsang, and J. T. Kwok · 2008
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Fast approximate k k NN graph construction for high dimensional data via recursive Lanczos bisection
J. Chen, H.-R. Fang, and Y. Saad · 2009
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
T. Hastie, R. Tibshirani, and J. Friedman · 2009
Cited alongside, same era.
Stochastic approximation of score functions for Gaussian processes
M. L. Stein, J. Chen, and M. Anitescu · 2013
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Kernel methods match deep neural networks on TIMIT
P. Huang, H. Avron, T. N. Sainath, V. Sindhwani, and B. Ramabhadran · 2014
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ASKIT: Approximate skeletonization kernel-independent treecode in high dimensions
W. B. March, B. Xiao, and G. Biros · 2014
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Memory efficient kernel approximation
S. Si, C.-J. Hsieh, and I. Dhillon · 2014
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Limitations on low rank approximations for covariance matrices of spatial data
M. L. Stein · 2014
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Random Laplace feature maps for semigroup kernels on histograms
J. Yang, V. Sindhwani, Q. Fan, H. Avron, and M. Mahoney · 2014
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Sparse random feature algorithm as coordinate descent in Hilbert space
I. Yen, T. Lin, S. Lin, P. Ravikumar, and I. Dhillon · 2014
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High-performance kernel machines with implicit distributed optimization and randomization
H. Avron and V. Sindhwani · 2016
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Faster kernel ridge regression using sketching and preconditioning
H. Avron, K. Clarkson, and D. Woodruff · 2016
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Memory efficient kernel approximation
S. Si, C.-J. Hsieh, and I. S. Dhillon · 2017
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An n log n n\log n parallel fast direct solver for kernel matrices
C. D. Yu, W. B. March, and G. Biros · 2017
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