Randomized nonlinear component analysis

31st International Conference on Machine Learning, ICML 2014

Volumn 4, Issue , 2014, Pages 3196-3204

  Lopez Paz, David ^a   Sra, Suvrit ^b   Smola, Alexander J ^b   Ghahramani, Zoubin ^a   Schölkopf, Bernhard ^c  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b CARNEGIE MELLON UNIVERSITY   (United States)

^c MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; LEARNING SYSTEMS; MULTIVARIANT ANALYSIS; NONLINEAR ANALYSIS;

CANONICAL CORRELATION ANALYSIS; CLASSICAL METHODS; COMPUTATIONAL REQUIREMENTS; MULTI VARIATE ANALYSIS; NONLINEAR COMPONENTS; RANDOMIZED METHOD; RECENT RESEARCHES; SPECTRAL CLUSTERING;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84919825559     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (34)

1. Achlioptas, D., McSherry, F., and Scholkopf, B. Sampling techniques for kernel methods. NIPS, 2002.
2. Andrew, G., Arora, R., Livescu, K., and Bilmes, J. Deep canonical correlation analysis. ICML, 2013.
3. Arora, R. and Livescu, K. Kernel CCA for multi-view learning of acoustic features using articulatory measurements. MLSLP, 2012.
4. Avron, H., Boutsidis, C., Toledo, S., and Zouzias, A. Efficient dimensionality reduction for canonical correlation analysis. ICML, 2013.
5. Bach, F. R. and Jordan, M. I. Kernel independent component analysis. JMLR, 2002.
6. Baglama, J. and Reichel, L. Restarted block lanczos bidiag- onalization methods. Numerical Algorithms, 2006.
7. Baldi, P. and Hornik, K. Neural networks and principal component analysis: Learning from examples without local minima. Neural Networks, 1989.
8. Bie, T. De, Cristianini, N., and Rosipal, R. Eigen problems in pattern recognition. Handbook of Geometric Computing, 2005.
9. Chaudhuri, K., Kakade, S. M., Livescu, K., and Sridha- ran, K. Multi-view clustering via canonical correlation analysis. ICML, 2009.
10. Hamid, R., Xiao, Y., Gittens, A., and DeCoste, D. Compact random feature maps. ICML, 2014.
11. Hinton, G. E. and Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science, 2006.
12. Hotelling, H. Relations Between Two Sets of Variates. Biometrika, 1936.
13. Jolliffe, I. T. Principal Component Analysis. Springer, 2002.
14. Kakade, S. M. and Foster, D. R Multi-view regression via canonical correlation analysis. COLT, 2007.
15. Kar, R and Karnick, H. Random feature maps for dot product kernels. arXiv: 1201.6530, 2012.
16. Lai, R and Fyfe, C. Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems, 2000.
17. Lampert, C. H., Nickisch, H., and Harmeling, S. Learning to detect unseen object classes by between class attribute transfer. CVPR, 2009.
18. Le, Q., Sarlos, T., and Smola, A. Fast food - Approximating kernel expansions in loglinear time. ICML, 2013.
19. LeCun, Y. and Cortes, C. The MNIST database of handwritten digits. 1998.
20. Lopez-Paz, D., Hennig, P., and Scholkopf, B. The Randomized Dependence Coefficient. NIPS, 2013.
21. Luxburg, U. A tutorial on spectral clustering. Statistics and Computing, 2007.
22. Mackey, L., Jordan, M. I., Chen, R. Y., Farrell, B., and Tropp, J. A. Matrix Concentration Inequalities via the Method of Exchangeable Pairs. Annals of Probability, 2014.
23. Mahoney, M. W. Randomized algorithms for matrices and data. Foundations and Trends in Machine Learning, 2011.
24. McWilliams, B., Balduzzi, D., and Buhmann, J. Correlated random features for fast semi-supervised learning. NIPS, 2013.
25. Pearson, K. On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 1901.
26. Rahimi, A. and Recht, B. Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning. NIPS, 2008.
27. Scholkopf, B. and Smola, A. J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2002.
28. Scholkopf, B., Smola, A., and Miiller, K. R. Kernel principal component analysis. Advances in kernel methods - Support vector learning, 1999.
29. Tropp, J. A. User-Friendly Tools for Random Matrices: An Introduction. NIPS Tutorials, 2012.
30. Vapnik, V. and Vashist, A. A new learning paradigm: Learning using privileged information. Neural Networks, 2009.
31. Westbury, J. R. X-Ray micro beam speech production database user's handbook version 1.0. 1994.
32. Williams, C. and Seeger, M. Using the Nystrom method to speed up kernel machines. NIPS, 2001.
33. Yang, J., Sindhwani, V., Avron, H., and Mahoney, M. W. Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. ICML, 2014.
34. Yang, T., Li, Y., Mahdavi, M., Jin, R., and Zhou, Z. Nystrom method vs random Fourier features: A theoretical and empirical comparison. NIPS, 2012.