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Crime event embedding with unsupervised feature selection

Abstract: 

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 ℓ1 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.

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Paper Details

Authors:
Shixiang Zhu, Yao Xie
Submitted On:
14 April 2019 - 4:12pm
Short Link:
Type:
Poster
Event:
Presenter's Name:
Yao Xie
Paper Code:
3112
Document Year:
2019
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Poster of the paper "Crime event embedding with unsupervised feature selection"

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[1] Shixiang Zhu, Yao Xie, "Crime event embedding with unsupervised feature selection", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3891. Accessed: Jun. 24, 2019.
@article{3891-19,
url = {http://sigport.org/3891},
author = {Shixiang Zhu; Yao Xie },
publisher = {IEEE SigPort},
title = {Crime event embedding with unsupervised feature selection},
year = {2019} }
TY - EJOUR
T1 - Crime event embedding with unsupervised feature selection
AU - Shixiang Zhu; Yao Xie
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3891
ER -
Shixiang Zhu, Yao Xie. (2019). Crime event embedding with unsupervised feature selection. IEEE SigPort. http://sigport.org/3891
Shixiang Zhu, Yao Xie, 2019. Crime event embedding with unsupervised feature selection. Available at: http://sigport.org/3891.
Shixiang Zhu, Yao Xie. (2019). "Crime event embedding with unsupervised feature selection." Web.
1. Shixiang Zhu, Yao Xie. Crime event embedding with unsupervised feature selection [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3891