@ARTICLE{6542036, 
author={M. Tan and I. W. Tsang and L. Wang}, 
journal={IEEE Transactions on Neural Networks and Learning Systems}, 
title={Minimax Sparse Logistic Regression for Very High-Dimensional Feature Selection}, 
year={2013}, 
volume={24}, 
number={10}, 
pages={1609-1622}, 
keywords={bioinformatics;minimax techniques;probability;regression analysis;minimax sparse logistic regression;very high-dimensional feature selection;strong convexity;probabilistic underpinnings;minimax sparse LR model;cutting plane algorithm;resultant nonsmooth minimax subproblems;smoothing coordinate descent method;synthetic datasets;real-world datasets;l1-regularized LR;bioinformatics;Feature selection;minimax problem;single-nucleotide polymorphism (SNP) detection;smoothing method;sparse logistic regression}, 
doi={10.1109/TNNLS.2013.2263427}, 
ISSN={2162-237X}, 
month={Oct},}