Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso

TL;DR

This paper introduces GFlasso, a graph-guided fused lasso method for structured multi-task regression that leverages output graph structures and an efficient optimization algorithm, outperforming traditional methods in scalability and accuracy.

Contribution

The paper proposes a novel graph-guided fused lasso model and an efficient proximal-gradient optimization method for structured multi-task regression with complex output relationships.

Findings

GFlasso outperforms standard lasso in accuracy.

The proposed method is faster and more scalable.

Experimental results confirm the effectiveness of GFlasso.

Abstract

We consider the problem of learning a structured multi-task regression, where the output consists of multiple responses that are related by a graph and the correlated response variables are dependent on the common inputs in a sparse but synergistic manner. Previous methods such as l1/l2-regularized multi-task regression assume that all of the output variables are equally related to the inputs, although in many real-world problems, outputs are related in a complex manner. In this paper, we propose graph-guided fused lasso (GFlasso) for structured multi-task regression that exploits the graph structure over the output variables. We introduce a novel penalty function based on fusion penalty to encourage highly correlated outputs to share a common set of relevant inputs. In addition, we propose a simple yet efficient proximal-gradient method for optimizing GFlasso that can also be applied to any optimization problems with a convex smooth loss and the general class of fusion penalty defined on arbitrary graph structures. By exploiting the structure of the non-smooth ''fusion penalty'', our method achieves a faster convergence rate than the standard first-order method, sub-gradient method, and is significantly more scalable than the widely adopted second-order cone-programming and quadratic-programming formulations. In addition, we provide an analysis of the consistency property of the GFlasso model. Experimental results not only demonstrate the superiority of GFlasso over the standard lasso but also show the efficiency and scalability of our proximal-gradient method.