New subsampling algorithms for fast least squares regression

Advances in Neural Information Processing Systems

Volumn , Issue , 2013, Pages

  Dhillon, Paramveer S ^a   Lu, Yichao ^b   Foster, Dean ^b   Ungar, Lyle ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COVARIANCE MATRIX;

FAST ESTIMATION; LARGE AMOUNTS OF DATA; LEAST SQUARES REGRESSION; LOSS OF ACCURACY; ORDINARY LEAST SQUARES; REAL-WORLD DATASETS; THEORETICAL BOUNDS; TWO-STAGE ESTIMATIONS;

ALGORITHMS;

EID: 84898936636     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Boutsidis, C., Gittens, A.: Improved matrix algorithms via the subsampled randomized hadamard transform. CoRR abs/1204.0062 (2012)
2. Tygert, M.: A fast algorithm for computing minimal-norm solutions to underdetermined systems of linear equations. CoRR abs/0905.4745 (2009)
3. Rokhlin, V., Tygert, M.: A fast randomized algorithm for overdetermined linear least-squares regression. Proceedings of the National Academy of Sciences 105(36) (September 2008) 13212-13217
4. Drineas, P., Mahoney, M.W., Muthukrishnan, S., Sarlós, T.: Faster least squares approximation. CoRR abs/0710.1435 (2007)
5. Mahoney, M.W.: Randomized algorithms for matrices and data. (April 2011)
6. Ailon, N., Chazelle, B.: Approximate nearest neighbors and the fast johnson-lindenstrauss transform. In: STOC. (2006) 557-563
7. Avron, H., Maymounkov, P., Toledo, S.: Blendenpik: Supercharging lapack's least-squares solver. SIAM J. Sci. Comput. 32(3) (April 2010) 1217-1236
8. Vershynin, R.: How Close is the Sample Covariance Matrix to the Actual Covariance Matrix? Journal of Theoretical Probability 25(3) (September 2012) 655-686
9. Golub, G.H., Van Loan, C.F.: Matrix Computations (Johns Hopkins Studies in Mathematical Sciences)(3rd Edition). 3rd edn. The Johns Hopkins University Press (October 1996)
10. Vershynin, R.: Introduction to the non-asymptotic analysis of random matrices. CoRR abs/1011.3027 (2010)
11. Dhillon, P.S., Rodu, J., Foster, D., Ungar, L.: 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)
12. Dhillon, P.S., Foster, D., Ungar, L.: Multi-view learning of word embeddings via cca. In: Advances in Neural Information Processing Systems (NIPS). Volume 24. (2011)
13. Shalev-Shwartz, S., Zhang, T.: Stochastic dual coordinate ascent methods for regularized loss minimization. CoRR abs/1209.1873 (2012)