Faculty Advisor
Paffenroth, Randy Clinton
Abstract
In this MQP project, our focus is on inverse methods for non-linear manifold learning. Methods for taking high-dimensional data and non-linearly projecting it to low-dimensions are well known. However, methods for going the other direction are less well studied. Here, we examine the use of linear and nonlinear methods of dimension reduction and study the particulars of the inverse mapping. Furthermore, we study which combinations of parameters are more effective when there are restrictions on our projections' dimension. We explore a variety of different projection algorithms including Principal Component Analysis, Isomap, and Local Tangent Space Alignment. Finally, the effectiveness of our approaches will be demonstrated on the image recognition problem of classifying handwritten digits.
Publisher
Worcester Polytechnic Institute
Date Accepted
May 2016
Major
Mathematical Sciences
Project Type
Major Qualifying Project
Copyright Statement
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Accessibility
Unrestricted
Advisor Department
Mathematical Sciences