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 this article yet. Authors can add summaries to their articles on Science...
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SLEP: Sparse Learning with Efficient Projections. http://www.public.asu.edu/~jye02/Software/SLEP Ma, S.Q., Xue, L.Z., Zou, H., 2013. Alternating direction methods for latent variable Gaussian graphical model selection. Neur. Comput., 25(8): 2172–2198. http://dx.doi.org/10.1162/...
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, ...
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 ...
Chandra, 2008, Efficient projections onto the 1-ball for learning in high dimensions: Proceedings of the 25th International Conference on Machine Learning, ACM, 272–279. [Gubernatis et al., 1977] Gubernatis, J., E. Domany, J. Krumhansl, and M. Huberman, 1977, The Born approximation ...
projections for sparse polygenic prediction from machine learning Timothy G. Raben 1*, Louis Lello 1,2, Erik Widen 1,2 & Stephen D. H. Hsu 1,2 In this paper we characterize the performance of linear models trained via widely-used sparse machine learning ...