Mark W. Tygert, Ph.D.

Affiliations: 
2004 Yale University, New Haven, CT 
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"Mark Tygert"

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Vladimir Rokhlin grad student 2004 Yale
 (Numerical aspects of Bessel transforms and of prolate spheroidal wave functions.)
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Publications

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Tygert M. (2019) Regression-aware decompositions Linear Algebra and Its Applications. 565: 208-224
Li H, Kluger Y, Tygert M. (2018) Randomized algorithms for distributed computation of principal component analysis and singular value decomposition. Advances in Computational Mathematics. 44: 1651-1672
Li H, Kluger Y, Tygert M. (2018) Randomized algorithms for distributed computation of principal component analysis and singular value decomposition Advances in Computational Mathematics. 44: 1651-1672
Li H, Linderman GC, Szlam A, et al. (2017) Algorithm 971: An Implementation of a Randomized Algorithm for Principal Component Analysis. Acm Transactions On Mathematical Software. Association For Computing Machinery. 43
Szlam A, Tulloch A, Tygert M. (2017) Accurate Low-Rank Approximations Via a Few Iterations of Alternating Least Squares Siam Journal On Matrix Analysis and Applications. 38: 425-433
Tygert M, Bruna J, Chintala S, et al. (2016) A Mathematical Motivation for Complex-Valued Convolutional Networks. Neural Computation. 1-11
Perkins W, Tygert M, Ward R. (2014) Some deficiencies of χ2 and classical exact tests of significance Applied and Computational Harmonic Analysis. 36: 361-386
Halko N, Martinsson PG, Shkolnisky Y, et al. (2011) An algorithm for the principal component analysis of large data sets Siam Journal On Scientific Computing. 33: 2580-2594
Coakley ES, Rokhlin V, Tygert M. (2011) A fast randomized algor ithm for orthogonal projection Siam Journal On Scientific Computing. 33: 849-868
Perkins W, Tygert M, Ward R. (2011) Computing the confidence levels for a root-mean-square test of goodness-of-fit Applied Mathematics and Computation. 217: 9072-9084
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