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Some of the material in is restricted to members of the community. By logging in, you may be able to gain additional access to certain collections or items. If you have questions about access or logging in, please use the form on the Contact Page.
Artificial neural networks (ANNs) are very popular nowadays and offer reliable solutions to many classification problems. Recent research indicates that these neural networks might be overparameterized and different solutions have been...
Symmetric positive definite (SPD) matrices have become fundamental computational objects in many areas. It is often of interest to average a collection of symmetric positive definite matrices. This dissertation investigates different...
Chan-Vese is a level set method that simultaneously evolves a level set surface and fits locally constant intensity models for the interior and exterior regions to minimize a Mumford-Shah integral. However, the length-based contour...
Quasi-Newton methods have gained popularity across various domains, providing efficient iterative algorithms for finding optimal solutions to unconstrained optimization problems. Their limited- memory variants offer advantages in terms...
This dissertation proposes a Riemannian approach for computing geodesics for closed curves in elastic shape space. The application of two Riemannian unconstrained optimization algorithms, Riemannian Steepest Descent (RSD) algorithm and...
This dissertation considers the optimization problems that are in the form of minX∈Fv f(x)+λ∥X∥1, where f is smooth, Fv = {X ∈ Rn×q : XTX = Iq, v ∈ span(X)}, and v is a given positive vector. Clustering analysis is a fundamental machine...
This dissertation uses Riemannian optimization theory to increase our understanding of the role extraction problem and algorithms. Recent ideas of using the low-rank projection of the neighborhood pattern similarity measure and our...
Clustering is a widely used technique with a long and rich history in a variety of areas. However, most existing algorithms do not scale well to large datasets, or are missing theoretical guarantees of convergence. In this dissertation, ...
In the foreseeable future, autonomous vehicles will have to drive alongside human drivers. In the absence of vehicle-to-vehicle communication, they will have to be able to predict the other road users' intentions. Equally importantly, ...
The problem of estimating trend and seasonality has been studied over several decades, although mostly using single time series setup. This dissertation studies the problem of estimating these components from a functional data point of...
Artificial Neural Networks form the basis of very powerful learning methods. It has been observed that a naive application of fully connected neural networks often leads to overfitting. In an attempt to circumvent this issue, a prior...
Some of the material in is restricted to members of the community. By logging in, you may be able to gain additional access to certain collections or items. If you have questions about access or logging in, please use the form on the Contact Page.
