Michael Bryant (Committee Member), Fred Garber (Committee Member), Randolph Moses (Committee Member), Brian Rigling (Advisor), Kefu Xue (Committee Member)
Doctor of Philosophy (PhD)
In this dissertation, a novel algorithm, SV-Means, is developed motivated by the many functions needed to perform radar waveform classification in an evolving, contested environment. Important functions include the ability to: reject classes not in the library, provide confidence in the classification decision, adapt the decision boundary on-the-fly, discover new classes, and quickly add new classes to the library. The SV-Means approach addresses these functions by providing a fast algorithm that can be used for anomaly detection, density estimation, open set classification, and clustering, within a Bayesian generative framework. The SV-Means algorithm extends the quantile one-class support vector machine (q-OCSVM) density estimation algorithm into a classification formulation with inspiration from k-means and stochastic gradient descent principles. In addition, the algorithm can be trained at least an order of magnitude faster than the q-OCSVM and other OCSVM algorithms. SV-Means has been thoroughly tested with a phase-modulated radar waveform data set, and several data sets from the University of California Irvine (UCI) machine learning repository, in each application area except clustering. In clustering, a novel algorithm, SV-Means Level Set Clustering, was formulated using the SV-Means algorithm as a first step to determine the number of clusters per level set and distinguish overlapping clusters. Finally, an end-to-end demonstration from training, to testing, to clustering, to adding a new class to the library, was demonstrated using the SV-Means algorithm.
Department or Program
Department of Electrical Engineering
Year Degree Awarded
Copyright 2017, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.