matlab - matlab中使用L1正则化的高效逻辑回归

Question

我正在 matlab 中寻找一个有效的逻辑回归实现。我在matlab中使用了lassoglm。但是当我尝试使用 10000 个具有 1000 个特征和正则化参数为 0.005 到 1 的示例时，它真的很慢。我使用两次交叉验证。从 lambda 0.05 开始，它非常慢并且需要很多时间。

有没有更好的方法？

Answer 1

您可能想查看LIBLINEAR。它是一个免费的、最先进的线性大规模学习库。它有一个 MATLAB 接口。

LIBLINEAR 具有多种线性方法，包括：

 for multi-class classification
     0 -- L2-regularized logistic regression (primal)
     1 -- L2-regularized L2-loss support vector classification (dual)
     2 -- L2-regularized L2-loss support vector classification (primal)
     3 -- L2-regularized L1-loss support vector classification (dual)
     4 -- support vector classification by Crammer and Singer
     5 -- L1-regularized L2-loss support vector classification
     6 -- L1-regularized logistic regression
     7 -- L2-regularized logistic regression (dual)
   for regression
    11 -- L2-regularized L2-loss support vector regression (primal)
    12 -- L2-regularized L2-loss support vector regression (dual)
    13 -- L2-regularized L1-loss support vector regression (dual)