Multivariate Analysis of fMRI using Fast Simultaneous Training of Generalized Linear Models (FaSTGLZ)

We present an efficient algorithm for simultaneously training elastic-net-regularized generalized linear models across many related problems, which may arise from bootstrapping, cross-validation and nonparametric permutation testing. Our approach leverages the redundancies across problems to obtain ≈ 10x computational improvements relative to solving the problems sequentially by the standard glmnet algorithm of (Friedman et al., 2010). We demonstrate our fast simultaneous training of generalized linear models (FaSTGLZ) algorithm, for multivariate analysis of fMRI and run otherwise computationally intensive bootstrapping and permutation test analyses that are typically necessary for obtaining statistically rigorous classification results and meaningful interpretation.

Accepted 1 July 2012
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