A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression
arXiv:1311.6529
Abstract
In this paper we purpose a blockwise descent algorithm for group-penalized multiresponse regression. Using a quasi-newton framework we extend this to group-penalized multinomial regression. We give a publicly available implementation for these in R, and compare the speed of this algorithm to a competing algorithm --- we show that our implementation is an order of magnitude faster than its competitor, and can solve gene-expression-sized problems in real time.
Cited by in corpus (7)
- Individualized Prediction of COVID-19 Adverse outcomes with MLHO
- Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data
- Selection of variables and decision boundaries for functional data via bi-level selection
- Instance Credibility Inference for Few-Shot Learning
- Group-sparse SVD Models and Their Applications in Biological Data
- How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learning
- Efficient and robust high-dimensional sparse logistic regression via nonlinear primal-dual hybrid gradient algorithms