Fast and Robust Multi-View Multi-Task Learning via Group Sparsity
Fast and Robust Multi-View Multi-Task Learning via Group Sparsity
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DOI：
10.24963/ijcai.2019/485
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发表时间：
2019-08
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通讯作者：
Lu Sun;Canh Hao Nguyen;Hiroshi Mamitsuka
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作者：
Lu Sun;Canh Hao Nguyen;Hiroshi Mamitsuka
多视点多任务学习由于具有双重异构性，即每个任务具有来自多个视点的异质特征，并且可能通过公共视点与其他任务关联。现有的方法通常存在三个问题：1)缺乏消除噪声特征的能力；2)对视点一致性有严格的假设；3)忽略任务-视点离群点的可能存在。为了克服这些局限性，我们通过将特征参数分解成两个分量的和，提出了一种联合组稀疏性的健壮方法，其中一个保存了相关特征(对于问题1)，而灵活的视图一致性(对于问题2)，另一种是检测任务-视图离群点(对于问题3)。利用全局收敛的性质，我们提出了一种在线性时间复杂度的情况下求解优化问题的快速算法。在各种合成数据集和真实数据集上的大量实验证明了该方法的有效性。
Multi-view multi-task learning has recently attracted more and more attention due to its dual-heterogeneity, i.e.,each task has heterogeneous features from multiple views, and probably correlates with other tasks via common views.Existing methods usually suffer from three problems: 1) lack the ability to eliminate noisy features, 2) hold a strict assumption on view consistency and 3) ignore the possible existence of task-view outliers.To overcome these limitations, we propose a robust method with joint group-sparsity by decomposing feature parameters into a sum of two components,in which one saves relevant features (for Problem 1) and flexible view consistency (for Problem 2),while the other detects task-view outliers (for Problem 3).With a global convergence property, we develop a fast algorithm to solve the optimization problem in a linear time complexity w.r.t. the number of features and labeled samples.Extensive experiments on various synthetic and real-world datasets demonstrate its effectiveness.