[PDF] Top 20 Deterministic algorithms for the low rank approximation of matrices
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Deterministic algorithms for the low rank approximation of matrices
... Low rank approximations using the symmetric eigenvalue decomposition Objectives and key idea Formulation of the matrix eigenvalue problem Standard algorithm for the Hermitian eigendecomp[r] ... Voir le document complet
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Fast hierarchical algorithms for the low-rank approximation of matrices, with applications to materials physics, geostatistics and data analysis
... kind of computational scientists keep developing new models in order to simulate real-life phenomenon more accurately and at more realistic ...because of their complex nature, large dimensions and extreme ... Voir le document complet
202
A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Norm
... compute the leading eigenvector with communication cost of O(d + m) per pass over the dataset ...upon the O(dm) cost incurred by a naive distributed ...prove the sublinear convergence ... Voir le document complet
19
The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices
... Random matrices, Haar measure, principal components analysis, informa- tional limit, free probability, phase transition, random eigenvalues, random eigenvectors, random pertur- bation, sample covariance ...by ... Voir le document complet
28
Fast and Memory Optimal Low-Rank Matrix Approximation
... note the output of SLA and the optimal rank k approximation of M , ...on the data if the columns of M are revealed in a random order, and two passes if ... Voir le document complet
10
Computing Low-Rank Approximations of Large-Scale Matrices with the Tensor Network Randomized SVD
... established the existence of the singular value decomposition (SVD) in 1873 [2], 1 he probably had not foreseen that this matrix factorization would become a crucial tool in scientific computing and ... Voir le document complet
25
A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Norm
... sequence of map-reduce steps (Dean and Ghemawat, 2008). This allows the computation to be massively parallelized across the set of workers, while allowing a simple implemen- tation and fast ... Voir le document complet
22
A Proposition for Fixing the Dimensionality of a Laplacian Low-rank Approximation of any Binary Data-matrix
... - OF - THE - ART Spectral methods are used for optimally condensing and representing a set of objects in a space of lower di- mensionality than the number of their ...when ... Voir le document complet
5
Sparse Low Rank Approximation of Multivariate Functions – Applications in Uncertainty Quantification
... within low rank structure for high dimensional ...on the observation that a large class of functions encountered in practical applications are found to have sufficiently accurate ... Voir le document complet
128
Matrix Generation in Isogeometric Analysis by Low Rank Tensor Approximation
... MPI for Mathematics in the Sciences, Leipzig, Germany ...that the task of matrix assembly in Isogeometric Analysis (IGA) is more challenging than in the case of tra- ditional ... Voir le document complet
20
On a fixed-point algorithm for structured low-rank approximation and estimation of half-life parameters
... Terms— Low rank approximation, structured ma- trices, fixed-point ...consider the problem of approximating a given matrix F by a structured matrix ...is low rank and ... Voir le document complet
6
Block Low-Rank Matrices with Shared Bases: Potential and Limitations of the BLR^2 Format
... BLOCK LOW-RANK MATRICES WITH SHARED BASES: POTENTIAL AND LIMITATIONS OF THE BLR 2 FORMAT ∗ CLEVE ASHCRAFT † , ALFREDO BUTTARI ‡ , AND THEO MARY § ...class of data sparse ... Voir le document complet
22
Jacobi-type algorithm for low rank orthogonal approximation of symmetric tensors and its convergence analysis
... be the set of symmetric ones [11, 27], whose entries do not change under any permutation of in- ...dices. The identity matrix of size n is denoted by I n ...be the Stiefel ... Voir le document complet
20
Constant Modulus Algorithms via Low-Rank Approximation
... in the works of Godard [1] and Triechler and Agee [2] on blind ...works, the linear equalizer weight vector was computed by exploiting the constant modulus of the desired signal, ... Voir le document complet
12
Communication avoiding low rank approximation based on QR with tournament pivoting
... truncation rank of 10. The last image shows the 10 columns selected by ...gives the singular values of this image and their approximations obtained by the different ...a ... Voir le document complet
23
Computation and approximation of the inverse of relationship matrices between genotyped animals: Algorithms and Applications
... Improvements The algorithm described provides a new insight on inversion of ...round of recursion returns an approximation that is better than the previous one, with convergence ... Voir le document complet
157
Compressive PCA for Low-Rank Matrices on Graphs
... C. Low-Rank Recovery from Random Projections Throughout this work we assume that the graphs G c and G r for the complete data matrix Y are either available or can be constructed ... Voir le document complet
18
Coupled tensor low-rank multilinear approximation for hyperspectral super-resolution
... regions of recoverability. For STEREO, we choose ranks F = 50 and F = 100 as in [8], and for HySure we use the number of groundtruth materials as the number of ...metrics ... Voir le document complet
6
Sparse Representations and Low-Rank Tensor Approximation
... another of lower rank is in general an ill posed ...kind of approximation is mandatory in the presence of measurement errors or ...between the columns of loading ... Voir le document complet
19
Approximation algorithms for survivable network design
... (¸) of precisely s (¸) ≠ 1 leaf-Steiner nodes, and let g(¸) = {¸} fi OPTlf (¸) be the group of ¸ .... The mapping is constructed iteratively in a bottom-up fashion as ...maintain the ... Voir le document complet
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