Date of Original Version
Copyright 2013 by the author(s)
Abstract or Description
This paper considers the sparse Gaussian conditional random field, a discriminative extension of sparse inverse covariance estimation, where we use convex methods to learn a high-dimensional conditional distribution of outputs given inputs. The model has been proposed by multiple researchers within the past year, yet previous papers have been substantially limited in their analysis of the method and in the ability to solve large-scale problems. In this paper, we make three contributions: 1) we develop a second-order active-set method which is several orders of magnitude faster that previously proposed optimization approaches for this problem 2) we analyze the model from a theoretical standpoint, improving upon past bounds with convergence rates that depend logarithmically on the data dimension, and 3) we apply the method to large-scale energy forecasting problems, demonstrating state-of-the-art performance on two real-world tasks.
Journal of Machine Learning Research : Workshop and Conference Proceedings, 28, 3, 1265-1273.