We demonstrate an approach for accelerating calculation of the regularization path for L1 sparse logistic regression problems. We show the benefit of feature clustering as a preconditioning step for parallel block-greedy coordinate descent algorithms.
Revised: December 16, 2013 |
Published: December 6, 2012
Citation
Scherrer C., A. Tewari, M. Halappanavar, and D.J. Haglin. 2012.Feature Clustering for Accelerating Parallel Coordinate Descent. In Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems (NIPS 2012), December 3-6, 2012, Lake Tahoe, Nevada, edited by P. Bartlett, et al, 28-36. La Jolla, California:Neural Information Processing Systems Foundation.PNNL-SA-88340.