scientific article; zbMATH DE number 6860845
zbMath1442.62153arXiv1504.03183MaRDI QIDQ4637072
Stoyan Georgiev, Sayan Mukherjee, Gregory Darnell, Barbara E. Engelhardt
Publication date: 17 April 2018
Full work available at URL: https://arxiv.org/abs/1504.03183
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
dimension reductionKrylov subspace methodsrandomized algorithmslinear mixed modelsgenomicsrandom projectionslow-ranksupervisedgeneralized eigendecompositon
Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Genetics and epigenetics (92D10) Statistical aspects of big data and data science (62R07)
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- Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
- Bi-cross-validation of the SVD and the nonnegative matrix factorization
- Fisher lecture: Dimension reduction in regression
- Restarted block Lanczos bidiagonalization methods
- The Johnson-Lindenstrauss lemma and the sphericity of some graphs
- Population correlation and population kinship
- Quasiorthogonal dimension of Euclidean spaces
- Contour regression: a general approach to dimension reduction
- Learning theory estimates via integral operators and their approximations
- On early stopping in gradient descent learning
- Computing the SVD of a General Matrix Product/Quotient
- Matrix Algorithms
- Randomized algorithms for the low-rank approximation of matrices
- Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
- Randomized Algorithms for Matrices and Data
- Algorithm 971
- Extensions of Lipschitz mappings into a Hilbert space
- Spectral Algorithms for Supervised Learning
- A Randomized Algorithm for Principal Component Analysis
- Sliced Inverse Regression for Dimension Reduction
- On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
- Average Information REML: An Efficient Algorithm for Variance Parameter Estimation in Linear Mixed Models
- ARPACK Users' Guide
- An elementary proof of a theorem of Johnson and Lindenstrauss
- Efficient Algorithms for Computing a Strong Rank-Revealing QR Factorization
- Subspace Iteration Randomization and Singular Value Problems
- Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Comment
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