Multi-task learning via robust regularized clustering with non-convex group penalties
This addresses outlier handling in multi-task learning for data scientists, but it is incremental as it builds on existing clustering assumptions.
The paper tackles the problem of outlier tasks in multi-task learning by proposing MTLRRC, a method that incorporates robust regularization and non-convex group penalties to simultaneously cluster tasks and detect outliers, showing effectiveness in simulations and real data.
Multi-task learning (MTL) aims to improve estimation and prediction performance by sharing common information among related tasks. One natural assumption in MTL is that tasks are classified into clusters based on their characteristics. However, existing MTL methods based on this assumption often ignore outlier tasks that have large task-specific components or no relation to other tasks. To address this issue, we propose a novel MTL method called Multi-Task Learning via Robust Regularized Clustering (MTLRRC). MTLRRC incorporates robust regularization terms inspired by robust convex clustering, which is further extended to handle non-convex and group-sparse penalties. The extension allows MTLRRC to simultaneously perform robust task clustering and outlier task detection. The connection between the extended robust clustering and the multivariate M-estimator is also established. This provides an interpretation of the robustness of MTLRRC against outlier tasks. An efficient algorithm based on a modified alternating direction method of multipliers is developed for the estimation of the parameters. The effectiveness of MTLRRC is demonstrated through simulation studies and application to real data.