Efficient logistic regression with L1 regularization in matlab

Question

I am searching for an efficient logistic regression implementation in matlab. I used lassoglm in matlab. But when I try with 10000 examples with 1000 features and regularization params 0.005 to 1, it is really slow. I use two fold cross validation. Starting with lambda 0.05, it is very slow and takes a lot of time.

Is there any better method?

Answer 1

You might want to check out LIBLINEAR . It is a free, state-of-the-art library for linear large scale learning. It has a MATLAB interface.

LIBLINEAR features several linear methods, including:

 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)