Results 11 to 20 of about 256,417 (191)
Application of Sparse Representation in Bioinformatics
Frontiers in Genetics, 2021 Inspired by L1-norm minimization methods, such as basis pursuit, compressed sensing, and Lasso feature selection, in recent years, sparse representation shows up as a novel and potent data processing method and displays powerful superiority.
Shuguang Han +8 more
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Sparse Recovery Using Sparse Matrices [PDF]
Proceedings of the IEEE, 2010 In this paper, we survey algorithms for sparse recovery problems that are based on sparse random matrices. Such matrices has several attractive properties: they support algorithms with low computational complexity, and make it easy to perform incremental updates to signals.
Gilbert, Anna, Indyk, Piotr
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Pentadiagonal Companion Matrices
Special Matrices, 2016 The class of sparse companion matrices was recently characterized in terms of unit Hessenberg matrices. We determine which sparse companion matrices have the lowest bandwidth, that is, we characterize which sparse companion matrices are permutationally ...
Eastman Brydon, Vander Meulen Kevin N.
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Linear Algebra and its Applications, 2003 The sparsity of orthogonal matrices which have both a column and a row of nonzero is studied. In Section 2, the authors describe a rich family \(n\) by \(n\) orthogonal matrices, namely, those that are the product of \(n-1\) Givens rotations. They show that this family contains a sparsest fully indecomposable orthogonal matrix with a full row.
Cheon, Gi-Sang +4 more
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SparseM: A Sparse Matrix Package for R *
Journal of Statistical Software, 2003 SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the package is illustrated by a family of linear model fitting functions that implement least squares methods for problems with sparse design matrices ...
Roger Koenker, Pin Ng
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New flexible deterministic compressive measurement matrix based on finite Galois field
IET Image Processing, 2022 Nowadays, the deterministic construction of sensing matrices is a hot topic in compressed sensing. The coherence of the measurement matrix is an important research area in the design of deterministic compressed sensing.
Vahdat Kazemi +2 more
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Local Laws for Sparse Sample Covariance Matrices
Mathematics, 2022 We proved the local Marchenko–Pastur law for sparse sample covariance matrices that corresponded to rectangular observation matrices of order n×m with n/m→y (where y>0) and sparse probability npn>logβn (where β>0).
Alexander N. Tikhomirov +1 more
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Exhaustive Search for Various Types of MDS Matrices
IACR Transactions on Symmetric Cryptology, 2019 MDS matrices are used in the design of diffusion layers in many block ciphers and hash functions due to their optimal branch number. But MDS matrices, in general, have costly implementations. So in search for efficiently implementable MDS matrices, there
Abhishek Kesarwani +2 more
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Inference for High-dimensional Differential Correlation Matrices [PDF]
, 2015 Motivated by differential co-expression analysis in genomics, we consider in this paper estimation and testing of high-dimensional differential correlation matrices.
Cai, T. Tony, Zhang, Anru
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Subset Selection in Sparse Matrices [PDF]
SIAM Journal on Optimization, 2020 In subset selection we search for the best linear predictor that involves a small subset of variables. From a computational complexity viewpoint, subset selection is NP-hard and few classes are known to be solvable in polynomial time. Using mainly tools from discrete geometry, we show that some sparsity conditions on the original data matrix allow us ...
Alberto Del Pia +2 more
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