Sparse approximation (also known as sparse representation) theory deals with sparse solutions for systems of linear equations. Techniques for finding...

Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims at finding a sparse representation of the...

special cases of the sparse general Vecchia approximation. These methods approximate the true model in a way the covariance matrix is sparse. Typically, each...

feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple...

Cholesky factorization of a symmetric positive definite matrix is a sparse approximation of the Cholesky factorization. An incomplete Cholesky factorization...

Matching pursuit (MP) is a sparse approximation algorithm which finds the "best matching" projections of multidimensional data onto the span of an over-complete...

(abbreviated as ILU) of a matrix is a sparse approximation of the LU factorization often used as a preconditioner. Consider a sparse linear system A x = b {\displaystyle...

Cholesky factorization — sparse approximation to the Cholesky factorization Incomplete LU factorization — sparse approximation to the LU factorization...

Ho, Jinn; Hwang, Wen-Liang (2020). "Refinement and Universal Approximation via Sparsely Connected ReLU Convolution Nets". IEEE Signal Processing Letters...

roughly 100,000 neurons. Other models are based on matching pursuit, a sparse approximation algorithm which finds the "best matching" projections of multidimensional...

hierarchical matrices (H-matrices) are used as data-sparse approximations of non-sparse matrices. While a sparse matrix of dimension n {\displaystyle n} can be...

Zero-truncated Poisson distribution Compound Poisson distribution Sparse approximation Hurdle model pscl, glmmTMB and brms R packages Bilder, Christopher;...

allows for imaging of electron beam sensitive materials. Noiselet Sparse approximation Sparse coding Low-density parity-check code Compressed sensing in speech...

the California Institute of Technology. He is known for work on sparse approximation, numerical linear algebra, and random matrix theory. Tropp studied...

Originally, shearlets were introduced in 2006 for the analysis and sparse approximation of functions f ∈ L 2 ( R 2 ) {\displaystyle f\in L^{2}(\mathbb {R}...

a function of many variables Hierarchical matrix, a data-sparse approximation of a non-sparse matrix Hilbert matrix, a square matrix with entries being...

weighted completion time Block Sorting (Sorting by Block Moves) Sparse approximation Variations of the Steiner tree problem. Specifically, with the discretized...

different when restricted to special classes of graphs. For instance, for sparse graphs (graphs in which the number of edges is at most a constant times...

shearlets have been used as a representation system to provide sparse approximations of this model class in 2D and 3D. Piecewise defined functions are...

Lasso (statistics) Least-squares spectral analysis Matching pursuit Sparse approximation A. M. Tillmann Equivalence of Linear Programming and Basis Pursuit...

S2CID 18432970. Joel A. Tropp (2004). "Greed is good: Algorithmic results for sparse approximation" (PDF). CiteSeerX 10.1.1.84.5256. Mutual coherence R1magic : R package...

[better source needed] Sparse approximation Singular value decomposition Matrix norm k-means clustering Low-rank approximation Michal Aharon; Michael...

In mathematics, low-rank approximation is a minimization problem, in which the cost function measures the fit between a given matrix (the data) and an...

independent sets in sparse graphs, a case that does not make sense for the complementary clique problem, there has also been work on approximation algorithms that...

Sylvain Fischer, Rafael Redondo, Laurent Perrinet, Gabriel Cristobal. Sparse approximation of images inspired from the functional architecture of the primary...

randomized rounding is a widely used approach for designing and analyzing approximation algorithms. Many combinatorial optimization problems are computationally...

meaningful gene sets Statistical learning theory Regularization Sparse approximation Proximal gradient methods Convex analysis Feature selection Rosasco...

deconvolution, are ill-posed. Variants of this method have been used also in sparse approximation problems and compressed sensing settings. Landweber, L. (1951): An...

large-scale dataset, including sparse principal component analysis and sparse matrix approximation. nsprcomp - R package for sparse and/or non-negative PCA based...

algorithm using dynamic programming. There is a fully polynomial-time approximation scheme, which uses the pseudo-polynomial time algorithm as a subroutine...