SLEP: sparse learning with efficient projections.Author(s): J. Liu, S Ji, J Ye, J Liu Publication date: 2009 Journal: Arizona State Univ. Read this article at ScienceOpen Bookmark There is no author summary for
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Liu J, Ji S, Ye J: SLEP: Sparse Learning with Efficient Projections. 2009, Arizona State University, http://www.public.asu.edu/~jye02/Software/SLEP, Google Scholar Llano DA, Laforet G, Devanarayan V: Derivation of a New ADAS-cog composite using tree-based multivariate analysis: predicti...
Learning sparse deep neural networks using efficient structured projections on convex constraints for green AIdoi:10.1109/ICPR48806.2021.9412162Training,Gradient methods,Neural networks,Pattern recognition,Computational efficiency,Projection algorithms,Artificial intelligence...
There has been a significant amount of research to develop efficient algo- rithms for solving the Sparse Dictionary Learning problem [3]. These algorithms typically consist of repeating two optimization steps. In the first step, a linear regression problem with the sparsity-inducing regularization ...
[280] with Symmetric Positive Definite (SPD) matrices. – Sparse Linear Approximation/Regression: This formulation problem is similar to sparse dictionary learning and leads to the same decomposition. First, Dikmen et al. [281–283] refer to linear approximation of the sparse error estimation, ...
Computer Science - LearningWe study the use of very sparse random projections for compressed sensing(sparse signal recovery) when the signal entries can be either positive ornegative. In our setting, the entries of a Gaussian design matrix are randomlysparsified so that only a very small fraction...
In this paper we characterize the performance of linear models trained via widely-usedsparsemachine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most ...
PHIST can not only use plain GHOST as its basic linear algebra layer; it is also equipped with fallback kernel implemen- tations and adapters for the Trilinos and Anasazi libraries. A major achievement in the development of PHIST was an efficient block Jacobi–Davidson eigenvalue solver, which ...
researchers and engineers, sparse learning has become a popular tool with the development of theoretical frameworks and various efficient algorithms. The theoretical frameworks range from the original idea in underdetermined linear systems (see Brucksteinet al.(2009) and references therein), signal ...