Langmead, B., C. Trapnell, M. Pop, and S.L Salzberg. 2009. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology 10(3):R25.

Leskovec, J., K.J. Lang, A. Dasgupta, and M.W. Mahoney. 2009. Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. Internet Mathematics 6(1):29-123.

Mahoney, M.W. 2008. Algorithmic and statistical challenges in modern large-scale data analysis are the focus of MMDS 2008. ACM SIGKDD Explorations Newsletter, December 20.

Mahoney, M.W., and P. Drineas. 2009. CUR matrix decompositions for improved data analysis. Proceedings of the National Academy of Sciences U.S.A. 106:697-702.

Mahoney, M.W., and L. Orecchia. 2011. Implementing regularization implicitly via approximate eigenvector computation. Pp. 121-128 in Proceedings of the 28th International Conference on Machine Learning (ICML). ICML, Bellevue, Wash.

Paschou, P., E. Ziv, E.G. Burchard, S. Choudhry, W. Rodriguez-Cintron, M.W. Mahoney, and P. Drineas. 2007. PCA-correlated SNPs for structure identification in worldwide human populations. PLoS Genetics 3:1672-1686.

Roweis, S., and L. Saul. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323-2326.

Salomon, D. 1998. Data Compression: The Complete Reference. Springer Verlag, London, U.K.

Spielman, D.A. 2010. Algorithms, graph theory, and linear equations in Laplacian matrices. Pp. 24-26 in Proceedings of the 39th International Congress of Mathematicians, Part II. Springer-Verlag, Berlin.

Tenenbaum, J.B., V. de Silva, and J.C. Langford. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319-2323.

Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B 58(1):267-288.

van der Maaten, L.J.P., E.O. Postma, and H.J. van den Herik. 2009. Dimensionality Reduction: A Comparative Review. Technical Report TiCC-TR 2009-005. Tilburg University, The Netherlands.

Vempala, S.S. 2005. The Random Projection Method. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Volume 65. AMS.

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement