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searching for Low-rank approximation 13 found (26 total)

Russian Geometric Kernel (412 words) [view diff] exact match in snippet view article find links to article

First Full-Featured Version". ledas.com. Gatilov, S. (2014). "Using low-rank approximation of the Jacobian matrix in the Newton-Raphson method to solve certain

Interpolative decomposition (345 words) [view diff] exact match in snippet view article find links to article

Rokhlin, V., & Tygert, M. (2007). Randomized algorithms for the low-rank approximation of matrices. Proceedings of the National Academy of Sciences, 104(51)

Non-uniform discrete Fourier transform (2,442 words) [view diff] exact match in snippet view article find links to article

based on oversampling and interpolation, min-max interpolation, and low-rank approximation. In general, NUFFTs leverage the FFT by converting the nonuniform

Euclidean distance matrix (2,226 words) [view diff] no match in snippet view article find links to article

distances can then be found by semidefinite approximation (and low rank approximation, if desired) using linear algebraic tools such as singular value

Basis pursuit denoising (562 words) [view diff] exact match in snippet view article find links to article

"Forward Backward Algorithm". Archived from the original on February 16, 2014. A list of BPDN solvers at the sparse- and low-rank approximation wiki.

Christiaan Heij (509 words) [view diff] exact match in snippet view article find links to article

the Mathematics Genealogy Project Markovsky, Ivan. "Structured low-rank approximation and its applications[permanent dead link]." Automatica 44.4 (2008):

Daniel Kressner (601 words) [view diff] exact match in snippet view article find links to article

linear eigenvalue problems, nonlinear eigenvalue problems, and low-rank approximation techniques for matrix problems. He has been awarded a second Leslie

K-means clustering (7,371 words) [view diff] no match in snippet view article find links to article

Madalina (2014). "Dimensionality reduction for k-means clustering and low rank approximation (Appendix B)". arXiv:1410.6801 [cs.DS]. Fukunaga, K.; Hostetler

Support vector machine (8,838 words) [view diff] exact match in snippet view article find links to article

avoid solving a linear system involving the large kernel matrix, a low-rank approximation to the matrix is often used in the kernel trick. Another common

Gaussian process approximations (1,982 words) [view diff] exact match in snippet view article find links to article

approach can often be represented as a repeated application of a low-rank approximation to successively smaller subsets of the index set X{\displaystyle

LU decomposition (6,254 words) [view diff] no match in snippet view article find links to article

} which is an LUP decomposition of A. It is possible to find a low rank approximation to an LU decomposition using a randomized algorithm. Given an input

Tensor rank decomposition (6,259 words) [view diff] case mismatch in snippet view article find links to article

Lim, L. (2008). "Tensor Rank and the Ill-Posedness of the Best Low-Rank Approximation Problem". SIAM Journal on Matrix Analysis and Applications. 30 (3):

Kernel embedding of distributions (9,130 words) [view diff] exact match in snippet view article find links to article

Gram matrix may be computationally demanding. Through use of a low-rank approximation of the Gram matrix (such as the incomplete Cholesky factorization)