Mason Lary

Graduation Semester and Year

2022

Language

English

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Engineering

First Advisor

Vassilis Athitsos

Abstract

Given a database of objects and a query object, it’s possible to gather a number of the closest neighbors to the query object. This operation is important to a number of diverse fields such as computer vision, content- based information retrieval, and chemistry. However, distance measures used to determine neighbors can cause queries to be computationally expensive, either because the distance measure is complex or because it is nonmetric and prevents efficient indexing methods. This work presents novel methods of triplet mining that enable neural networks using triplet loss to learn the manifold that data resides in. These neural networks can learn to embed arbitrary data with arbitrary distance measures between them. Experiments are performed on an offline digit dataset, speech commands, and offline and online sign language data. Results demonstrate effectiveness over a baseline when a network architecture suited for a particular dataset is trained. When compared to other methods of topology preserving embeddings, the neural network based method outperforms in all but one dataset. Results show there is not a particular method of triplet mining that vastly outperforms the others, and the best method likely depends on the problem being addressed.

Keywords

Triplet mining, Triplet loss, Neural networks, dimensionality reduction

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

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