Date of Award


Document Type


Degree Name

Master of Science (MS)


Computer Engineering and Sciences

First Advisor

Adrian Peter

Second Advisor

Anthony Smith

Third Advisor

Luis Daniel Otero

Fourth Advisor

Muzaffar Shaikh


This thesis presents a novel framework for time series classification that leverages the geometric structure of covariance matrices when labeling signals. Our method maps each signal to a new multivariate localized feature signal (MLFS) representation, from which we compute a covariance descriptor. This robust MLFS covarieance representation handles classification tasks where the sampling rates of the signals vary within a class or classes. We demonstrate that by simply using the k-nearest neighbor classification rule and multiclass kernel support vector machine with the Riemannian metric between the MLFS convariance matrices, which produces state-of-the-art results on a number of standard datasets. Moreover, for the first time, we showcase results on the full library of typical infrasonic signals dataset, which contains four categories of infrasound observations.