A Fused Elastic Net Logistic Regression Model for Multi-Task Binary Classification

TL;DR

This paper introduces a fused elastic net logistic regression model for multi-task binary classification, leveraging sparsity penalties to improve performance across related tasks and efficiently optimize the joint model.

Contribution

It proposes a novel sparse multi-task learning approach with a recursive ADMM optimization method, enhancing binary classification performance across related tasks.

Findings

Significant improvements over single-task learning in synthetic data tests.

Efficient optimization via recursive ADMM.

Applicable to various real-world multi-task classification problems.

Abstract

Multi-task learning has shown to significantly enhance the performance of multiple related learning tasks in a variety of situations. We present the fused logistic regression, a sparse multi-task learning approach for binary classification. Specifically, we introduce sparsity inducing penalties over parameter differences of related logistic regression models to encode similarity across related tasks. The resulting joint learning task is cast into a form that lends itself to be efficiently optimized with a recursive variant of the alternating direction method of multipliers. We show results on synthetic data and describe the regime of settings where our multi-task approach achieves significant improvements over the single task learning approach and discuss the implications on applying the fused logistic regression in different real world settings.