Physically Motivated Feature Development for Machine Learning Applications

Abstract

Feature development forms a cornerstone of many machine learning applications. In this work, we develop features, motivated by physical or physiological knowledge, for several applications: energy disaggregation, brain cancer prognosis, and landmine detection with seismo-acoustic vibrometry (SAVi) sensors. For event-based energy disaggregation, or the automated process of extracting component specific energy data from a building's aggregate power signal, we develop low dimensional features that capture transient information near changes in energy signals. These features reflect the circuit composition of devices comprising the aggregate signal and enable classifiers to discriminate between devices. To develop image based biomarkers, which may help clinicians develop treatment strategies for patients with glioblastoma brain tumors, we exploit physiological evidence that certain genes are both predictive of patient survival and correlated with tumor shape. We develop features that summarize tumor shapes and therefore serve as surrogates for the genetic content of tumors, allowing survival prediction. Our final analysis and the main focus of this document is related to landmine detection using SAVi sensors. We exploit knowledge of both landmine shapes and the interactions between acoustic excitation and the ground's vibration response to develop features that are indicative of target presence. Our analysis, which employs these novel features, is the first evaluation of a large dataset recorded under realistic conditions and provides evidence for the utility of SAVi systems for use in combat arenas.