IEEE Trans Cybern. 2018 Aug;48(8):2284-2293. doi: 10.1109/TCYB.2017.2732818. Epub 2017 Aug 3.
Multitask learning (MTL) aims to learn multiple related tasks simultaneously instead of separately to improve the generalization performance of each task. Most existing MTL methods assumed that the multiple tasks to be learned have the same feature representation. However, this assumption may not hold for many real-world applications. In this paper, we study the problem of MTL with heterogeneous features for each task. To address this problem, we first construct an integrated graph of a set of bipartite graphs to build a connection among different tasks. We then propose a non-negative matrix factorization-based multitask method (MTNMF) to learn a common semantic feature space underlying different heterogeneous feature spaces of each task. Moreover, an improved version of MTNMF (IMTNMF) is proposed, in which we do not need to construct the correlation matrix between input features and class labels, avoiding the information loss. Finally, based on the common semantic features and original heterogeneous features, we model the heterogenous MTL problem as a multitask multiview learning (MTMVL) problem. In this way, a number of existing MTMVL methods can be applied to solve the problem effectively. Extensive experiments on three real-world problems demonstrate the effectiveness of our proposed methods, and the improved version IMTNMF can gain about 2% average accuracy improvement compared with MTNMF.
多任务学习（MTL）旨在同时学习多个相关任务，而不是分别学习，以提高每个任务的泛化性能。大多数现有的 MTL 方法都假设要学习的多个任务具有相同的特征表示。然而，对于许多实际应用来说，这种假设可能并不成立。在本文中，我们研究了每个任务具有异构特征的 MTL 问题。为了解决这个问题，我们首先构建了一组二分图的集成图，以建立不同任务之间的联系。然后，我们提出了一种基于非负矩阵分解的多任务方法（MTNMF），以学习不同任务的异构特征空间下的公共语义特征空间。此外，还提出了一种改进的 MTNMF（IMTNMF），其中我们不需要构建输入特征和类别标签之间的相关矩阵，避免了信息丢失。最后，基于公共语义特征和原始异构特征，我们将异构 MTL 问题建模为多任务多视图学习（MTMVL）问题。这样，许多现有的 MTMVL 方法就可以有效地应用于解决该问题。在三个真实问题上的广泛实验验证了我们提出的方法的有效性，改进后的 IMTNMF 方法与 MTNMF 相比平均可以提高约 2%的准确率。
