# Crime Event Embedding with Unsupervised Feature Selection

arXiv: https://arxiv.org/abs/1806.06095 · HTML: https://mlsanity.com/paper/1806.06095/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2018.

## Built on by (most cited first, 3 of 3)

- [Black-Box Optimization with Implicit Constraints for Public Policy](https://mlsanity.com/paper/2310.18449/) (2023)
- [Imitation Learning of Neural Spatio-Temporal Point Processes](https://mlsanity.com/paper/1906.05467/) (2019)
- [Advancing Crime Linkage Analysis with Machine Learning: A Comprehensive Review and Framework for Data-Driven Approaches](https://mlsanity.com/paper/2411.00864/) (2024)

## Abstract (arXiv)

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.