【摘要】 对高维流式数据的在线组变量选择问题进行了研究，提出了带Group Lasso惩罚的逻辑斯蒂回归在线估计方法，并给出了GFTPRL (Group Follow the Proximally Regularized Leader)算法。通过给出GFTPRL算法的缺憾界，证明了算法在理论上是有效的。实验结果表明，对于稀疏模型GFTPRL算法的预测分类准确率明显优于其他主流稀疏在线算法。更多还原

【Abstract】 Aiming at solving the Group Lasso of high-dimensional data or streaming data, the online learning model for the group lasso is proposed, and a closed-form solution of this model is obtained. Then the GFTPRL(Group Follow the Proximally Regularized Leader) algorithm is applied to logistic regression. Moreover, the GFTPRL algorithm’s regret bound is proved to be good in online framework. Finally, the numerical results show that the prediction accuracy of the GFTPRL algorithm is significantly better than that of other mainstream sparse online algorithms when the sample size is large and the final model is sparse.更多还原