An eigenvector-based hotspot detection
This work addresses hotspot detection for spatiotemporal systems, but it appears incremental as it applies existing tensor and eigenvector techniques to this domain.
The paper tackled the problem of detecting anomalies in spatiotemporal data by proposing the SST-Hotspot algorithm, which uses tensor decomposition and eigenvector matching to account for variations and identify hotspots, with experimental results demonstrating its application in hotspot analysis.
Space and time are two critical components of many real world systems. For this reason, analysis of anomalies in spatiotemporal data has been a great of interest. In this work, application of tensor decomposition and eigenspace techniques on spatiotemporal hotspot detection is investigated. An algorithm called SST-Hotspot is proposed which accounts for spatiotemporal variations in data and detect hotspots using matching of eigenvector elements of two cases and population tensors. The experimental results reveal the interesting application of tensor decomposition and eigenvector-based techniques in hotspot analysis.