Sparse Online Learning via Truncated Gradient

Details Sparse Online Learning via Truncated Gradient Sparse Online Learning via Truncated Gradient

2008-06-28

arxiv.org/abs/0806.4686v2

Computer Science

Learning

Scientific paper

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: The degree of sparsity is continuous -- a parameter controls the rate of sparsification from no sparsification to total sparsification. 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 sparsification result in only small additional regret with respect to typical online learning guarantees. The approach works well empirically. We apply the approach to several datasets and find that for datasets with large numbers of features, substantial sparsity is discoverable.

Affiliated with

Also associated with

No associations

Rating

Sparse Online Learning via Truncated Gradient does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Sparse Online Learning via Truncated Gradient, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Sparse Online Learning via Truncated Gradient will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-624785