Fast Simultaneous Training of Generalized Linear Models (FaSTGLZ)
This work addresses efficiency challenges in statistical analysis for researchers needing rigorous classification results, though it appears incremental as it builds on existing methods.
The authors tackled the problem of computationally intensive simultaneous training of sparse generalized linear models for tasks like bootstrapping and permutation testing, and developed the FaSTGLZ algorithm that leverages redundancies across problems to achieve significant computational improvements.
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.