We propose a general method called truncated gradient to induce sparsity in the weights of online learning algorithms with convex loss functions. This method has several essential properties: 1. The degree of sparsity is continuousa parameter controls the rate of sparsication from no sparsication to total sparsication. 2. The approach is theoretically motivated, and an instance of it can be regarded as an online counterpart of the popular L 1 -regularization method in the batch setting. We prove that small rates of sparsication result in only small additional regret with respect to typical online learning guarantees. 3. The approach works well empirically. We apply the approach to several datasets and nd that for datasets with large numbers of features, substantial sparsity is discoverable.
Truncated gradient is also used to induce sparsity in the learned weights of the online learning algorithm REF .
Sparse Online Learning via Truncated Gradient
ArXiv
ArXiv
Mathematics Computer Science