The method assumes that the observed data can be well fit using a sparse linear combination of tensors taken from a fixed collection of possible tensors each having a different orientation.
Restricting our analysis to DQ2.5+ individuals, we trained two new GRSs, one using a sparse linear model of SNPs only (GRS-DQ2.5), and another built similarly to GRS-DQ2.5 but also utilizing markers imputed by SNP2HLA (GRS-DQ2.5-imputed).
To estimate a graph G, the training points for the n th gene, D n, are used to solve a sparse linear regression problem posed as a LASSO and formulated as an LP.
In the second stage, spectral bisection for large-scale problems is realized using a sparse direct linear solver to narrow down the interval of the k-th eigenvalue.
We propose an efficient and robust global numerical method, based on a method of lines and Differential Algebraic Equations (DAE) solvers, combined with a Newton method using a powerful sparse linear solver.
In the linear feature adaptation approach, a sparse linear transform, called cross transform, is used to transform multiple frames of speech coefficients to a new feature space.
The 3D multi-group neutron diffusion equations are expressed in terms of a sparse linear system which can be solved using different iterative Krylov subspace solvers.
To find the shared low-dimensional subspace across multiple data types, Shen et al. proposed a latent model iCluster + based on probabilistic principal component analysis, which used generalized linear models to transform continuous, discretized and count variables as a sparse linear regression on a set of latent driving factors.
Our key contribution is a local/global algorithm, which combines a local mapping of each 3D triangle to the plane, using transformations taken from a restricted set, with a global "stitch" operation of all triangles, involving a sparse linear system.
In [12], under the assumption that the chrominance image is given as a sparse linear combination of the basis constructed from the luminance image, the algorithm achieves a high compression performance using the theory of compressed sensing [13, 14].
