Walter Scott, Jr. College of Engineering

Graduate Exam Abstract

Nathan Larson
M.S. Final
Mar 31, 2021, 3:00 pm - 5:00 pm
A Multi-task Learning Method using Gradient Descent with Applications
Abstract: There is a critical need to develop classification methods that can robustly and accurately classify different objects in varying environments. Each environment in a classification problem can contain its own unique challenges which prevent traditional classifiers from performing well. To solve classification problems in different environments, multi-task learning (MTL) models have been applied that define each environment as a separate task. We discuss two existing MTL algorithms and explain how they are inefficient for situations involving high-dimensional data. A gradient descent-based MTL algorithm is proposed which allows for high-dimensional data while providing accurate classification results. Additionally, we introduce a kernelized MTL algorithm which may allow us to generate nonlinear classifiers.
We compared our proposed MTL method with an existing method, Efficient Lifelong Learning Algorithm (ELLA), by using them to train classifiers on the underwater unexploded ordnance (UXO) and extended modified National Institute of Standards and Technology (EMNIST) datasets. The UXO dataset contained acoustic color features of low-frequency sonar data. Both real data collected from physical experiments as well as synthetic data were used forming separate environments. The EMNIST digits dataset contains grayscale images of handwritten digits. We used this dataset to show how our proposed MTL algorithm performs when used with more tasks than are in the UXO dataset.
Our classification experiments showed that our gradient descent-based algorithm resulted in improved performance over those of the traditional methods. The UXO dataset had a small improvement while the EMNIST dataset had a much larger improvement when using our MTL algorithm compared to ELLA and the single task learning method.
Adviser: Mahmood Azimi-Sadjadi
Co-Adviser: N/A
Non-ECE Member: Iuliana Oprea, MATH
Member 3: Ali Pezeshki, ECE
Addional Members: N/A
N. Larson, J. Hall, and M. R. Azimi-Sadjadi. "Analyzing Transfer Learning Methods For UXO Classification In Varying Shallow Water Environments." 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2019.
Program of Study:
ECE 513
ECE 514
ECE 516
ECE 520
ECE 652
ECE 653
ECE 699
MATH 560