Get ready for another seminar: A Fast Unified Algorithm for Solving Group-Lasso Penalize Learning Problems Yi Yang Thursday, January 15, 2015 10:30am, 300 Ford Hall Social at 10:15am, 300 Ford Hall m Abstract This paper concerns a class of group-lasso learning problems where the objective function is the sum of an empirical loss and the group-lasso penalty. For a class of loss function satisfying a quadratic majorization condition, we derive a unified algorithm called groupwise-majorization-descent (GMD) for efficiently computing the solution paths of the corresponding group-lasso penalized learning problem. GMD allows for general design matrices, without requiring the predictors to be group-wise orthonormal. As illustration examples, we develop concrete algorithms for solving the group-lasso penalized least squares and several group-lasso penalized large margin classifiers. These group-lasso models have been implemented in an R package gglasso publicly available from the Comprehensive R Archive Network (CRAN) at cran.r-project.org/web/ packages/gglasso. On simulated and real data, gglasso consistently outperforms the existing software for computing the group-lasso that implements either the classical groupwise descent algorithm or Nesterov’s method. We will also discuss an application of the algorithm in the non-life insurance industry.
Posted on: Wed, 14 Jan 2015 17:15:15 +0000
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