Multi-task learning regression via convex clustering

• Published in: Computational statistics & data analysis Vol. 195; p. 107956
• Main Authors: Okazaki, Akira, Kawano, Shuichi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2024
• Subjects: Block-wise coordinate descent; Convex clustering; Logistic regression; Multi-task learning; Network lasso; Regularization; Block-wise coordinate descent; Logistic regression; Multi-task learning; Regularization; Network lasso; Convex clustering
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/j.csda.2024.107956

Multi-task learning (MTL) is a methodology that aims to improve the general performance of estimation and prediction by sharing common information among related tasks. In the MTL, there are several assumptions for the relationships and methods to incorporate them. One of the natural assumptions in the practical situation is that tasks are classified into some clusters with their characteristics. For this assumption, the group fused regularization approach performs clustering of the tasks by shrinking the difference among tasks. This enables the transfer of common information within the same cluster. However, this approach also transfers the information between different clusters, which worsens the estimation and prediction. To overcome this problem, an MTL method is proposed with a centroid parameter representing a cluster center of the task. Because this model separates parameters into the parameters for regression and the parameters for clustering, estimation and prediction accuracy for regression coefficient vectors are improved. The effectiveness of the proposed method is shown through Monte Carlo simulations and applications to real data.