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Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics.
On lines and planes of closest fit to systems of points in space
Pearson, K · 1901
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Relations Between Two Sets of Variates
Hotelling, H · 1936
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Neural networks and principal component analysis: Learning from examples without local minima
Baldi, P. and Hornik, K · 1989
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X-Ray microbeam speech production database user’s handbook version 1.0
Westbury, J. R · 1994
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The MNIST database of handwritten digits
LeCun, Y. and Cortes, C · 1998
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Kernel principal component analysis
Schölkopf, B., Smola, A., and Müller, K. R · 1999
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Kernel and nonlinear canonical correlation analysis
Lai, P. and Fyfe, C · 2000
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Using the Nyström method to speed up kernel machines
Williams, C. and Seeger, M · 2001
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Sampling techniques for kernel methods
Achlioptas, D., McSherry, F., and Schölkopf, B · 2002
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Kernel independent component analysis
Bach, F. and Jordan, M. I · 2002
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Principal Component Analysis
Jolliffe, I. T · 2002
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Schölkopf, B. and Smola, A. J · 2002
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Eigenproblems in pattern recognition
Bie, T. De, Cristianini, N., and Rosipal, R · 2005
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Restarted block lanczos bidiagonalization methods
Baglama, J. and Reichel, L · 2006
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Reducing the dimensionality of data with neural networks
Hinton, G. E. and Salakhutdinov, R. R · 2006
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Multi-view regression via canonical correlation analysis
Kakade, S. M. and Foster, D. P · 2007
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A tutorial on spectral clustering
Luxburg, U · 2007
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Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning
Rahimi, A. and Recht, B · 2008
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Random feature maps for dot product kernels
Kar, P. and Karnick, H · 2012
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User-Friendly Tools for Random Matrices: An Introduction
Tropp, J. A · 2012
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Nyström method vs random Fourier features: A theoretical and empirical comparison
Yang, T., Li, Y., Mahdavi, M., Jin, R., and Zhou, Z · 2012
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Deep canonical correlation analysis
Andrew, G., Arora, R., Livescu, K., and Bilmes, J · 2013
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Efficient dimensionality reduction for canonical correlation analysis
Avron, H., Boutsidis, C., Toledo, S., and Zouzias, A · 2013
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Fastfood – Approximating kernel expansions in loglinear time
Le, Q., Sarlos, T., and Smola, A · 2013
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Multi-view clustering via canonical correlation analysis
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Learning to detect unseen object classes by betweenclass attribute transfer
Lampert, C. H., Nickisch, H., and Harmeling, S · 2009
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A new learning paradigm: Learning using privileged information
Vapnik, V. and Vashist, A · 2009
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Randomized algorithms for matrices and data
Mahoney, M. W · 2011
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Arora, R. and Livescu, K · 2012
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The Randomized Dependence Coefficient
Lopez-Paz, D., Hennig, P., and Schölkopf, B · 2013
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Correlated random features for fast semi-supervised learning
McWilliams, B., Balduzzi, D., and Buhmann, J · 2013
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Compact random feature maps
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Matrix Concentration Inequalities via the Method of Exchangeable Pairs
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Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
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