Crime Event Embedding with Unsupervised Feature Selection
Crime Event Embedding with Unsupervised Feature Selection
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DOI：
10.1109/icassp.2019.8682285
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Shixiang Zhu;Yao Xie
中科院分区：
文献类型：
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作者：
Shixiang Zhu;Yao Xie
我们提出了一种新的事件嵌入算法的犯罪数据，可以共同捕捉时间，位置和复杂的自由文本组件的每个事件。嵌入是通过正则化限制玻尔兹曼机（RBM）实现的，我们引入了一种新的正则化方法，通过对RBM的观测变量的条件分布施加`1惩罚。这种正则化的选择执行特征选择，并且它还导致有效的计算，因为梯度可以以闭合形式计算。特征选择迫使嵌入基于最重要的关键字，这捕获了常见的操作方式（M.O.）犯罪系列通过对大规模犯罪数据集的数值实验，我们证明了我们的正则化RBM可以实现更好的事件嵌入，并且所选特征具有高度的人类理解可解释性。
We present a novel event embedding algorithm for crime data that can jointly capture time, location, and the complex free-text component of each event. The embedding is achieved by regularized Restricted Boltzmann Machines (RBMs), and we introduce a new way to regularize by imposing a `1 penalty on the conditional distributions of the observed variables of RBMs. This choice of regularization performs feature selection and it also leads to efficient computation since the gradient can be computed in a closed form. The feature selection forces embedding to be based on the most important keywords, which captures the common modus operandi (M.O.) in crime series. Using numerical experiments on a large-scale crime dataset, we show that our regularized RBMs can achieve better event embedding and the selected features are highly interpretable from human understanding.