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

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

In the mathematical theory of artificial neural networks, universal approximation theorems are theorems of the following form: Given a family of neural...

sparse coding R {\displaystyle R} with a given dictionary D {\displaystyle \mathbf {D} } is known as sparse approximation (or sometimes just sparse coding...

(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...

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

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

sensing in speech signals Low-density parity-check code Noiselet Sparse approximation Sparse coding Verification-based message-passing algorithms in compressed...

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

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

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...

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

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

In mathematics, low-rank approximation refers to the process of approximating a given matrix by a matrix of lower rank. More precisely, it is a minimization...

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...

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

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

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

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}...

improving approximate solutions for a class of problems, in which the i-th approximation (called an "iterate") is derived from the previous ones. A specific...

Least-squares spectral analysis Matching pursuit Sparse approximation Natarajan, B. K. (April 1995). "Sparse Approximate Solutions to Linear Systems". SIAM...

computes such transformations by factorizing the DFT matrix into a product of sparse (mostly zero) factors. As a result, it manages to reduce the complexity...

admissible solutions, increasing the generalizability of the function approximation. This way, embedding this prior information into a neural network results...

related fields, relaxation is a modeling strategy. A relaxation is an approximation of a difficult problem by a nearby problem that is easier to solve....

"Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection". arXiv:1102.3975 [stat.ML]. Liu et al....

decisions about how to construct the approximation. More technically, general versions of the approximation lead to a sparse Cholesky factor of the precision...

S2CID 18432970. Joel A. Tropp (2004). "Greed is good: Algorithmic results for sparse approximation" (PDF). CiteSeerX 10.1.1.84.5256. Welch, L. R. (1974). "Lower bounds...

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

won the Fields Medal for proving Roth's theorem on the Diophantine approximation of algebraic numbers. He was also a winner of the De Morgan Medal and...

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