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因子标记时间点过程的解耦学习
Decoupled Learning for Factorial Marked Temporal Point Processes |
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| 课程网址: |
http://videolectures.net/kdd2018_wang_decoupled_learning/
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| 主讲教师: |
Lu Wang
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| 开课单位: |
华东师范大学
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| 开课时间: |
2018-11-23
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| 课程语种: |
英语
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| 中文简介: |
本文介绍了因子标记时间点过程模型，并提出了有效的学习方法。在传统（多维）标记时间点过程模型中，事件通常由单个离散变量（即标记）编码。在本文中，我们描述了阶乘标记点过程，其中时间戳事件被分解为多个标记。因此，对成对标记之间的影响进行建模的传染性矩阵的大小与离散标记空间的数量成幂级数。我们提出了一种具有两个学习过程的解耦学习方法：i）基于两种技术直接求解模型：乘法器交替方向法和快速迭代收缩阈值算法；ii）涉及将原始问题转换为Logistic回归模型以进行更有效的学习的重新表述。此外，还添加了稀疏组正则化器来识别关键配置文件特征和事件标签。在真实世界数据集上的经验结果证明了我们的解耦和重构方法的效率。源代码可在线获取。
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| 课程简介: |
This paper introduces the factorial marked temporal point process model and presents efficient learning methods. In conventional (multi-dimensional) marked temporal point process models, event is often encoded by a single discrete variable i.e. a marker. In this paper, 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 power order w.r.t. the number of the discrete marker space. 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. The source code is available online.
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| 关 键 词: |
因子标记时间点; 过程的解耦学习; Logistic回归模型; 关键配置文件特征
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| 课程来源: |
视频讲座网
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| 数据采集: |
2023-03-09：cyh
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| 最后编审: |
2023-05-15：cyh
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| 阅读次数: |
27
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