We present an efficient algorithm for simultaneously training sparse 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 significant computational improvements relative to solving the problems sequentially by a conventional algorithm. We demonstrate our fast simultaneous training of generalized linear models (FaSTGLZ) algorithm on a number of real-world datasets, and we run otherwise computationally intensive bootstrapping and permutation test analyses that are typically necessary for obtaining statistically rigorous classification results and meaningful interpretation. Code is freely available at http://liinc.bme.columbia.edu/fastglz.
@article{arxiv.1307.8430,
title = {Fast Simultaneous Training of Generalized Linear Models (FaSTGLZ)},
author = {Bryan R. Conroy and Jennifer M. Walz and Brian Cheung and Paul Sajda},
journal= {arXiv preprint arXiv:1307.8430},
year = {2013}
}