Operations Research, Systems Engineering and Industrial Engineering Commons^™

Improving Non-Linear Approaches To Anomaly Detection, Class Separation, And Visualization, Todd J. Paciencia

Theses and Dissertations

Linear approaches for multivariate data analysis are popular due to their lower complexity, reduced computational time, and easier interpretation. In many cases, linear approaches produce adequate results; however, non-linear methods may generate more robust transformations, features, and decision boundaries. Of course, these nonlinear methods present their own unique challenges that often inhibit their use. In this research, improvements to existing non-linear techniques are investigated for the purposes of providing better, timely class separation and improved anomaly detection on various multivariate datasets, culminating in application to anomaly detection in hyperspectral imagery. Primarily, kernel-based methods are investigated, with some consideration towards other …