IEEE Trans Cybern. 2020 Jan;50(1):74-86. doi: 10.1109/TCYB.2018.2864107. Epub 2018 Aug 23.
Multitask feature selection (MTFS) methods have become more important for many real world applications, especially in a high-dimensional setting. The most widely used assumption is that all tasks share the same features, and the l regularization method is usually applied. However, this assumption may not hold when the correlations among tasks are not obvious. Learning with unrelated tasks together may result in negative transfer and degrade the performance. In this paper, we present a flexible MTFS by graph-clustered feature sharing approach. To avoid the above limitation, we adopt a graph to represent the relevance among tasks instead of adopting a hard task set partition. Furthermore, we propose a graph-guided regularization approach such that the sparsity of the solution can be achieved on both the task level and the feature level, and a variant of the smooth proximal gradient method is developed to solve the corresponding optimization problem. An evaluation of the proposed method on multitask regression and multitask binary classification problem has been performed. Extensive experiments on synthetic datasets and real-world datasets demonstrate the effectiveness of the proposed approach to capture task structure.
多任务特征选择（MTFS）方法在许多现实应用中变得越来越重要，尤其是在高维环境中。最广泛使用的假设是所有任务共享相同的特征，并且通常应用 l 正则化方法。然而，当任务之间的相关性不明显时，这种假设可能不成立。一起学习不相关的任务可能会导致负迁移并降低性能。在本文中，我们提出了一种灵活的 MTFS 方法，通过图聚类特征共享方法。为了避免上述限制，我们采用图来表示任务之间的相关性，而不是采用硬任务集分区。此外，我们提出了一种图引导正则化方法，使得解在任务级别和特征级别上都可以实现稀疏性，并开发了一种平滑近端梯度方法的变体来解决相应的优化问题。在多任务回归和多任务二分类问题上对所提出的方法进行了评估。在合成数据集和真实数据集上的广泛实验表明，所提出的方法能够有效地捕捉任务结构。
