论文标题
用于多任务学习的Semisoft任务群集
Semisoft Task Clustering for Multi-Task Learning
论文作者
论文摘要
多任务学习（MTL）旨在通过利用有用信息来提高多个相关预测任务的性能。由于它们的灵活性和可大大减少未知系数的能力，因此基于任务的MTL方法引起了极大的关注。我们提出了一种SemiSoft任务聚类方法，这是由SemiSoft数据聚类的想法的动机，该方法可以同时揭示纯任务和混合任务的任务群集结构，并选择相关功能。我们方法背后的主要假设是每个集群都有一些纯粹的任务，每个混合任务可以通过不同群集中纯任务的线性组合来表示。为了解决所得的非凸约限制优化问题，我们设计了有效的三步算法。基于合成和现实世界数据集的实验结果验证了所提出方法的有效性和效率。最后，我们将提出的方法扩展到一个强大的任务聚类问题。
Multi-task learning (MTL) aims to improve the performance of multiple related prediction tasks by leveraging useful information from them. Due to their flexibility and ability to reduce unknown coefficients substantially, the task-clustering-based MTL approaches have attracted considerable attention. Motivated by the idea of semisoft clustering of data, we propose a semisoft task clustering approach, which can simultaneously reveal the task cluster structure for both pure and mixed tasks as well as select the relevant features. The main assumption behind our approach is that each cluster has some pure tasks, and each mixed task can be represented by a linear combination of pure tasks in different clusters. To solve the resulting non-convex constrained optimization problem, we design an efficient three-step algorithm. The experimental results based on synthetic and real-world datasets validate the effectiveness and efficiency of the proposed approach. Finally, we extend the proposed approach to a robust task clustering problem.
