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Crime incidents embedding using Restricted Boltzmann machine

Abstract: 

We present a new approach for detecting related crime series, by unsupervised learning of the latent feature embeddings from narratives of crime record via the Gaussian-Bernoulli Restricted Boltzmann Machines (RBM). This is a drastically different approach from prior work on crime analysis, which typically considers only time and location and at most category information. After the embedding, related cases are closer to each other in the Euclidean feature space, and the unrelated cases are far apart, which is a good property can enable subsequent analysis such as detection and clustering of related cases. Experiments over several series of related crime incidents hand labeled by the Atlanta Police Department reveal the promise of our embedding methods.

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Paper Details

Authors:
Shixiang Zhu, Yao Xie
Submitted On:
14 April 2018 - 12:16am
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Shixiang Zhu
Paper Code:
3816
Document Year:
2018
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Document Files

ICASSP-Slides.pdf

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[1] Shixiang Zhu, Yao Xie, "Crime incidents embedding using Restricted Boltzmann machine", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2793. Accessed: Oct. 16, 2018.
@article{2793-18,
url = {http://sigport.org/2793},
author = {Shixiang Zhu; Yao Xie },
publisher = {IEEE SigPort},
title = {Crime incidents embedding using Restricted Boltzmann machine},
year = {2018} }
TY - EJOUR
T1 - Crime incidents embedding using Restricted Boltzmann machine
AU - Shixiang Zhu; Yao Xie
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2793
ER -
Shixiang Zhu, Yao Xie. (2018). Crime incidents embedding using Restricted Boltzmann machine. IEEE SigPort. http://sigport.org/2793
Shixiang Zhu, Yao Xie, 2018. Crime incidents embedding using Restricted Boltzmann machine. Available at: http://sigport.org/2793.
Shixiang Zhu, Yao Xie. (2018). "Crime incidents embedding using Restricted Boltzmann machine." Web.
1. Shixiang Zhu, Yao Xie. Crime incidents embedding using Restricted Boltzmann machine [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2793