2017/10/28 by Shixiang Zhu, Yao Xie, Zhu, Shixiang +1
Computer Science · Social Sciences · #Anomaly Detection Techniques and Applications #Crime Patterns and Interventions #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.1710.10513
openalex publication_date 2017/10/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.