Date of Award
12-2016
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Engineering and Sciences
First Advisor
Adrian Peter
Second Advisor
Anthony Smith
Third Advisor
Luis Daniel Otero
Fourth Advisor
Muzaffar Shaikh
Abstract
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.
Recommended Citation
Winsala, Oluwaseun, "Time Series Classification using Covariance Descriptors" (2016). Theses and Dissertations. 871.
https://repository.fit.edu/etd/871