Decoupled Learning for Factorial Marked Temporal Point Processes
Decoupled Learning for Factorial Marked Temporal Point Processes
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DOI：
10.1145/3219819.3220035
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发表时间：
2018-01
期刊：
影响因子：
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通讯作者：
Weichang Wu;Junchi Yan;Xiaokang Yang;H. Zha
中科院分区：
文献类型：
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作者：
Weichang Wu;Junchi Yan;Xiaokang Yang;H. Zha
本文提出了一种阶乘标记时间点过程模型，并给出了有效的学习方法。在传统的（多维）标记时间点过程模型中，事件通常由单个离散变量（标记）编码。我们描述了阶乘标记点的过程，其中的时间戳事件被分解成多个标记。因此，对成对标志物之间的效应进行建模的感染性矩阵的大小关于离散标志物的数量呈指数顺序。我们提出了一个解耦的学习方法，有两个学习过程：i）直接求解模型的基础上的两种技术：交替方向的方法乘数和快速迭代收缩-保持算法; ii）涉及到一个重新制定，将原来的问题转化为一个逻辑回归模型，以更有效的学习。此外，稀疏群正则化器被添加到识别的关键轮廓特征和事件标签。在真实的数据集上的实验结果证明了该方法的有效性。
This paper presents a factorial marked temporal point process model and presents efficient learning methods. In conventional (multi-dimensional) marked temporal point process models, an event is often encoded by a single discrete variable (marker). We describe the factorial marked point processes whereby time-stamped event is factored into multiple markers. Accordingly the size of the infectivity matrix modeling the effect between pairwise markers is in exponential order regarding the number of discrete markers. We propose a decoupled learning method with two learning procedures: i) directly solving the model based on two techniques: Alternating Direction Method of Multipliers and Fast Iterative Shrinkage-Thresholding Algorithm; ii) involving a reformulation that transforms the original problem into a Logistic Regression model for more efficient learning. Moreover, a sparse group regularizer is added to identify the key profile features and event labels. Empirical results on real world datasets demonstrate the efficiency of our decoupled and reformulated method.