Fast Implementation of l 1 Regularized Learning Algorithms Using Gradient Descent Methods ∗

With the advent of high-throughput technologies, l1 regularized learning algorithms have attracted much attention recently. Dozens of algorithms have been proposed for fast implementation, using various advanced optimization techniques. In this paper, we demonstrate that l1 regularized learning problems can be easily solved by using gradient-descent techniques. The basic idea is to transform a convex optimization problem with a non-differentiable objective function into an unconstrained non-convex problem, upon which, via gradient descent, reaching a globally optimum solution is guaranteed. We present detailed implementation of the algorithm using l1 regularized logistic regression as a particular application. We conduct large-scale experiments to compare the new approach with other stateof-the-art algorithms on eight medium and large-scale problems. We demonstrate that our algorithm, though simple, performs similarly or even better than other advanced algorithms in terms of computational efficiency and memory usage. Keyword: l1 regularized learning, feature selection, sparse solution, gradient descent