标题： 多阶段多任务特征学习 显示英文标题

标题： Multi-Stage Multi-Task Feature Learning

摘要： 多任务稀疏特征学习旨在通过利用任务之间的共享特征来提高泛化性能。 它已被成功应用于计算机视觉和生物医学信息学等多个应用领域。 现有的大多数多任务稀疏特征学习算法被表述为一个凸稀疏正则化问题，由于其对$\ell_0$类正则化的近似过于宽松，因此通常不是最优的。 在本文中，我们提出了一种基于新型非凸正则化的多任务稀疏特征学习的非凸公式。 为了解决非凸优化问题，我们提出了一种多阶段多任务特征学习（MSMTFL）算法；我们还对该算法提供了直观的解释，以及详细的收敛性和可重复性分析。 此外，我们提供了一个详细的理论分析，表明 MSMTFL 在参数估计误差界方面优于凸公式。 在合成数据集和真实世界数据集上的实证研究证明了 MSMTFL 相比于最先进的多任务稀疏特征学习算法的有效性。 显示英文摘要

摘要： Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated as a convex sparse regularization problem, which is usually suboptimal, due to its looseness for approximating an $\ell_0$-type regularizer. In this paper, we propose a non-convex formulation for multi-task sparse feature learning based on a novel non-convex regularizer. To solve the non-convex optimization problem, we propose a Multi-Stage Multi-Task Feature Learning (MSMTFL) algorithm; we also provide intuitive interpretations, detailed convergence and reproducibility analysis for the proposed algorithm. Moreover, we present a detailed theoretical analysis showing that MSMTFL achieves a better parameter estimation error bound than the convex formulation. Empirical studies on both synthetic and real-world data sets demonstrate the effectiveness of MSMTFL in comparison with the state of the art multi-task sparse feature learning algorithms.