# Crime incidents embedding using restricted Boltzmann machines

arXiv: https://arxiv.org/abs/1710.10513 · HTML: https://mlsanity.com/paper/1710.10513/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2017.

## Built on by (most cited first, 1 of 1)

- [Imitation Learning of Neural Spatio-Temporal Point Processes](https://mlsanity.com/paper/1906.05467/) (2019)

## Abstract (arXiv)

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.