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Overview over hardware and software requirements of the different functions for estimating L1-regularized linear models. The lasso function is part of the Statistics and Machine Learning Toolbox and only included here for benchmarking purposes (see main text). The lasso_mex, lasso_mexcuda and <t> lasso_gpu </t> functions introduced here are specifically optimized for the mass-univariate analysis approach and depend on different hardware and software configurations
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Overview over hardware and software requirements of the different functions for estimating L1-regularized linear models. The lasso function is part of the Statistics and Machine Learning Toolbox and only included here for benchmarking purposes (see main text). The lasso_mex, lasso_mexcuda and  lasso_gpu  functions introduced here are specifically optimized for the mass-univariate analysis approach and depend on different hardware and software configurations

Journal: Neuroinformatics

Article Title: Fast Estimation of L1-Regularized Linear Models in the Mass-Univariate Setting

doi: 10.1007/s12021-020-09489-1

Figure Lengend Snippet: Overview over hardware and software requirements of the different functions for estimating L1-regularized linear models. The lasso function is part of the Statistics and Machine Learning Toolbox and only included here for benchmarking purposes (see main text). The lasso_mex, lasso_mexcuda and lasso_gpu functions introduced here are specifically optimized for the mass-univariate analysis approach and depend on different hardware and software configurations

Article Snippet: lasso_gpu , CPU + CUDA-enabled GPU , Matlab, Parallel Computing Toolbox , ADMM , Matlab.

Techniques: Software