Large scale canonical correlation analysis with iterative least squares

Advances in Neural Information Processing Systems

Volumn 1, Issue January, 2014, Pages 91-99

  Lu, Yichao ^a   Foster, Dean P ^b  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b YAHOO RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATION THEORY; CORRELATION METHODS; INFORMATION SCIENCE; ITERATIVE METHODS; LEARNING SYSTEMS; STATISTICAL MECHANICS;

APPROXIMATION SCHEME; ASYMPTOTIC CONVERGENCE; CANONICAL CORRELATION ANALYSIS; ITERATIVE ALGORITHM; ITERATIVE LEAST SQUARES; MACHINE LEARNING PROBLEM; Q R DECOMPOSITION; STATISTICAL TOOLS;

SINGULAR VALUE DECOMPOSITION;

EID: 84937951105     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Marina A. Epelman. Rate of convergence of steepest descent algorithm. 2007.
2. Haim Avron, Christos Boutsidis, Sivan Toledo, and Anastasios Zouzias. Efficient dimensionality reduction for canonical correlation analysis. In ICML (1), pages 347-355, 2013.
3. Francis R. Bach and Michael I. Jordan. A probabilistic interpretation of canonical correlation analysis. Technical report, University of California, Berkeley, 2005.
4. Léon Bottou. Large-Scale Machine Learning with Stochastic Gradient Descent. In Yves Lechevallier and Gilbert Saporta, editors, Proceedings of the 19th International Conference on Computational Statistics (COMPSTAT'2010), pages 177-187, Paris, France, August 2010. Springer.
5. Paramveer Dhillon, Yichao Lu, Dean P. Foster, and Lyle Ungar. New subsampling algorithms for fast least squares regression. In Advances in Neural Information Processing Systems 26, pages 360-368. 2013.
6. Paramveer S. Dhillon, Dean Foster, and Lyle Ungar. Multi-view learning of word embeddings via cca. In Advances in Neural Information Processing Systems (NIPS), Volume 24, 2011.
7. Paramveer S. Dhillon, Jordan Rodu, Dean P. Foster, and Lyle H. Ungar. Two step cca: A new spectral method for estimating vector models of words. In Proceedings of the 29th International Conference on Machine learning, ICML'12, 2012.
8. Petros Drineas, Michael W. Mahoney, S. Muthukrishnan, and Tamás Sarlós. Faster least squares approximation. CoRR, abs/0710.1435, 2007.
9. Dean P. Foster, Sham M. Kakade, and Tong Zhang. Multi-view dimensionality reduction via canonical correlation analysis. Technical report, 2008.
10. Gene H. Golub and Charles F. Van Loan. Matrix Computations (3rd Ed.). Johns Hopkins University Press, Baltimore, MD, USA, 1996.
11. Gene. H Golub and Hongyuan Zha. The canonical correlations of matrix pairs and their numerical computation. Technical report, Computer Science Department, Stanford University, 1992.
12. N. Halko, P. G. Martinsson, and J. A. Tropp. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. SIAM Rev., 53(2):217-288, May 2011.
13. Nathan Halko, Per-Gunnar Martinsson, Yoel Shkolnisky, and Mark Tygert. An algorithm for the principal component analysis of large data sets. SIAM J. Scientific Computing, 33(5):2580-2594, 2011.
14. David R. Hardoon, Sandor Szedmak, Or Szedmak, and John Shawe-taylor. Canonical correlation analysis; an overview with application to learning methods. Technical report, 2007.
15. H Hotelling. Relations between two sets of variables. Biometrika, 28:312-377, 1936.
16. Rie Johnson and Tong Zhang. Accelerating stochastic gradient descent using predictive variance reduction. Advances in Neural Information Processing Systems (NIPS), 2013.
17. Sham M. Kakade and Dean P. Foster. Multi-view regression via canonical correlation analysis. In In Proc. of Conference on Learning Theory, 2007.
18. Michael Lamar, Yariv Maron, Mark Johnson, and Elie Bienenstock. SVD and Clustering for Unsupervised POS Tagging. In Proceedings of the ACL 2010 Conference Short Papers, pages 215-219, Uppsala, Sweden, 2010. Association for Computational Linguistics.
19. Justin Ma, Lawrence K. Saul, Stefan Savage, and Geoffrey M. Voelker. Identifying suspicious urls: An application of large-scale online learning. In In Proc. of the International Conference on Machine Learning (ICML), 2009.
20. Liang Sun, Shuiwang Ji, and Jieping Ye. A least squares formulation for canonical correlation analysis. In Proceedings of the 25th International Conference on Machine Learning, ICML '08, pages 1024-1031, New York, NY, USA, 2008. ACM.
21. Lloyd N. Trefethen and David Bau. Numerical Linear Algebra. SIAM, 1997.