A unified algorithm for fitting penalized models with high-dimensional data
Yi Yang · Sep 18, 2015
Date: 2015-09-18
Time: 15:30-16:30
Location: BURN 1205
Abstract:
In the light of high-dimensional problems, research on the penalized model has received much interest. Correspondingly, several algorithms have been developed for solving penalized high-dimensional models. I will describe fast and efficient unified algorithms for computing the solution path for a collection of penalized models. In particular, we will look at an algorithm for solving L1-penalized learning problems and an algorithm for solving group-lasso learning problems. These algorithm take advantage of a majorization-minimization trick to make each update simple and efficient. The algorithms also enjoy a proven convergence property. To demonstrate the generality of these algorithms, I extend them to a class of elastic net penalized large margin classification methods and to elastic net penalized Cox proportional hazards models. These algorithms have been implemented in three R packages gglasso, gcdnet and fastcox, which are publicly available from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/web/packages. On simulated and real data, our algorithms consistently outperform the existing software in speed for computing penalized models and often delivers better quality solutions.