Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning-猫眼课题宝

DOI：

10.24963/ijcai.2019/486

发表时间：

2019-08

期刊：

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其他

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作者：

Lu Sun;Canh Hao Nguyen;Hiroshi Mamitsuka

多视图多任务学习是指处理双异构数据，其中每个样本具有多视图特征，多个任务通过共同视图进行关联。现有方法不足以解决三个关键挑战：（a）有效保存任务相关性，（b）构建稀疏模型和（c）学习视图权重。在本文中，我们提出了一种基于乘法稀疏特征分解的新方法来直接处理这些挑战。对于（a），权重矩阵通过低秩分解为两个分量约束矩阵分解，通过学习减少数量的模型参数来保存任务相关性。对于（b）和（c），第一个组件进一步分解为两个子组件，分别选择特定于主题的特征并学习视图重要性。理论分析揭示了它与联合正则化的一般形式的等价性，并激励我们开发一种线性复杂度的快速优化算法。数据大小。在模拟和现实数据集上进行的大量实验验证了其效率。

Multi-view multi-task learning refers to dealing with dual-heterogeneous data,where each sample has multi-view features,and multiple tasks are correlated via common views.Existing methods do not sufficiently address three key challenges:(a) saving task correlation efficiently, (b) building a sparse model and (c) learning view-wise weights.In this paper, we propose a new method to directly handle these challenges based on multiplicative sparse feature decomposition.For (a), the weight matrix is decomposed into two components via low-rank constraint matrix factorization, which saves task correlation by learning a reduced number of model parameters.For (b) and (c), the first component is further decomposed into two sub-components,to select topic-specific features and learn view-wise importance, respectively. Theoretical analysis reveals its equivalence with a general form of joint regularization,and motivates us to develop a fast optimization algorithm in a linear complexity w.r.t. the data size.Extensive experiments on both simulated and real-world datasets validate its efficiency.

Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning

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